Journal of Mechanical Engineering no. 10 year 2021 volume 67 Strojniški vestnik – Journal of Mechanical Engineering (SV-JME) Aim and Scope The international journal publishes original and (mini)review articles covering the concepts of materials science, mechanics, kinematics, thermodynamics, energy and environment, mechatronics and robotics, .uid mechanics, tribology, cybernetics, industrial engineering and structural analysis. The journal follows new trends and progress proven practice in the mechanical engineering and also in the closely related sciences as are electrical, civil and process engineering, medicine, microbiology, ecology, agriculture, transport systems, aviation, and others, thus creating a unique forum for interdisciplinary or multidisciplinary dialogue. The international conferences selected papers are welcome for publishing as a special issue of SV-JME with invited co-editor(s). Editor in Chief Vincenc Butala University of Ljubljana, Faculty of Mechanical Engineering, Slovenia Technical Editor Pika Škraba University of Ljubljana, Faculty of Mechanical Engineering, Slovenia Founding Editor Bojan Kraut University of Ljubljana, Faculty of Mechanical Engineering, Slovenia Editorial O.ce University of Ljubljana, Faculty of Mechanical Engineering SV-JME, Aškerceva 6, SI-1000 Ljubljana, Slovenia Phone: 386 (0)1 4771 137 Fax: 386 (0)1 2518 567 info@sv-jme.eu, http://www.sv-jme.eu Print: Demat d.o.o., printed in 250 copies Founders and Publishers University of Ljubljana, Faculty of Mechanical Engineering, Slovenia University of Maribor, Faculty of Mechanical Engineering, Slovenia Association of Mechanical Engineers of Slovenia Chamber of Commerce and Industry of Slovenia, Metal Processing Industry Association President of Publishing Council Mitjan Kalin University of Ljubljana, Faculty of Mechanical Engineering, Slovenia Vice-President of Publishing Council Bojan Dolšak University of Maribor, Faculty of Mechanical Engineering, Slovenia Cover: The issue of intelligent production of special production tooling is one of the topics that relate to the Smart Factory paradigm. Using knowledge engineering techniques and advanced CAM system tools, it becomes possible to automate the process of preparing a machining program for entire families of similar products. The image shows the special tooling family - assembly equipment for plastic pipes, for which a method of automatic CAM programming, called ACPUT, was developed. The paper aims to present results of testing the e.ectiveness of the use of ACPUT by technicians with di.erent levels of experience (expert and beginner). Image Courtesy: Maciej Kowalski, MK-Tech Company, Poland, and Przemyslaw Zawadzki, Phd, Poznan University of Technology, Faculty of Mechanical Engineering, Poland International Editorial Board Kamil Arslan, Karabuk University, Turkey Ha.z Muhammad Ali, King Fahd U. of Petroleum & Minerals, Saudi Arabia Josep M. Bergada, Politechnical University of Catalonia, Spain Anton Bergant, Litostroj Power, Slovenia Miha Boltežar, University of Ljubljana, Slovenia Filippo Cianetti, University of Perugia, Italy Janez Diaci, University of Ljubljana, Slovenia Anselmo Eduardo Diniz, State University of Campinas, Brazil Igor Emri, University of Ljubljana, Slovenia Imre Felde, Obuda University, Faculty of Informatics, Hungary Imre Horvath, Delft University of Technology, The Netherlands Aleš Hribernik, University of Maribor, Slovenia Soichi Ibaraki, Kyoto University, Department of Micro Eng., Japan Julius Kaplunov, Brunel University, West London, UK Iyas Khader, Fraunhofer Institute for Mechanics of Materials, Germany Jernej Klemenc, University of Ljubljana, Slovenia Milan Kljajin, J.J. Strossmayer University of Osijek, Croatia Peter Krajnik, Chalmers University of Technology, Sweden Janez Kušar, University of Ljubljana, Slovenia Gorazd Lojen, University of Maribor, Slovenia Darko Lovrec, University of Maribor, Slovenia Thomas Lben, University of Bremen, Germany George K. Nikas, KADMOS Engineering, UK Tomaž Pepelnjak, University of Ljubljana, Slovenia Vladimir Popovic, University of Belgrade, Serbia Franci Pušavec, University of Ljubljana, Slovenia Mohammad Reza Safaei, Florida International University, USA Marco Sortino, University of Udine, Italy Branko Vasic, University of Belgrade, Serbia Arkady Voloshin, Lehigh University, Bethlehem, USA General information Strojniški vestnik – Journal of Mechanical Engineering is published in 11 issues per year (July and August is a double issue). 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We would like to thank the reviewers who have taken part in the peer-review ISSN 0039-2480, ISSN 2536-2948 (online) process. Š 2021 Strojniški vestnik - Journal of Mechanical Engineering. The journal is subsidized by Slovenian Research Agency. All rights reserved. SV-JME is indexed / abstracted in: SCI-Expanded, Compendex, Inspec, ProQuest-CSA, SCOPUS, Strojniški vestnik - Journal of Mechanical Engineering is available on TEMA. The list of the remaining bases, in which SV-JME is https://www.sv-jme.eu. indexed, is available on the website. Contents Strojniški vestnik - Journal of Mechanical Engineering volume 67, (2021), number 10 Ljubljana, October 2021 ISSN 0039-2480 Published monthly Papers Maciej Kowalski, Przemyslaw Zawadzki, Adam Hamrol: Effectiveness of Automatic CAM Programming Using Machining Templates for the Manufacture of Special Production Tooling Shuai Yang, Xing Luo, Chuan Li: Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network Zisheng Wang, Bo Li, Chao Liang, Xuewen Wang, Jiahao Li: Response Analysis of a Scraper Conveyor under Chain Faults Based on MBD-DEM-FEM Yong Chen, Jinjin Tan, Guoping Xiao: Investigation on the Depth of Slip Hanger Teeth Bite into Casing and the Mechanical Properties of Casing under Different Suspension Loads in Ultra-Deep Wells Saravanan Kandasamy Ganesan, Thanigaivelan Rajasekaran: Optimization of Laser Parameters and Dimple Geometry Using PCA-Coupled GRG Rajmohan Bose, Arunachalam Kandavel: Mechanical Characterization and Structural Attributes of Biohybrid Composites Derived Using Hemp, Bamboo, and Jute Fibres: An Alternative Approach in the Application of Natural Fibres in Automobile Parts 475 489 501 516 525 534 Strojniški vestnik - Journal of Mechanical Engineering 67(2021)10, 475-488 Received for review: 2021-05-28 Š 2021 Journal of Mechanical Engineering. All rights reserved. Received revised form: 2021-08-17 DOI:10.5545/sv-jme.2021.7285 Original Scientific Paper Accepted for publication: 2021-08-18 Effectiveness of Automatic CAM Programming Using Machining Templates for the Manufacture of Special Production Tooling Kowalski, M. – Zawadzki, P. – Hamrol, A. Maciej Kowalski1,2 – Przemyslaw Zawadzki1,* – Adam Hamrol1 1 Poznan University of Technology, Faculty of Mechanical Engineering, Poland 2 MK-Tech Company, Poland The paper presents the methodology and implementation of original Automatic CAM programming using machining templates (ACPUT) dedicated to manufacturing special technological tooling. The development of ACPUT was inspired by the observation that although modern computer-aided design (CAD) / computer-aided manufacturing (CAM) systems can automatically create CAM programs, their universality makes them both difficult to use and inefficient because the programs created this way often contain errors. The presented programming procedure includes the development of specific machining templates based on technological knowledge gathered in a specially prepared database. These templates are dedicated to a group of parts characterized by the similarity of their geometric features. ACPUT makes it possible to reduce(in comparison to traditional CAM programming) the time required to develop a machining program, thereby positively impacting the total cost of tooling production. The paper aims to present results of testing the effectiveness of the use of ACPUT by technicians with different levels of experience (expert and beginner). The tests were carried out on special tooling - assembly equipment for plastic pipes, and compared program preparation time, machining time, and production costs. Keywords: generative CAM process, CNC programming, technology design automation, knowledge-based engineering Highlights • Automatic CAM programming using machining templates (ACPUT) is dedicated to the manufacture of special technological tooling that consists of technologically similar parts. • ACPUT makes it possible to shorten the time needed to develop a machining program and to reduce the total cost of tooling production. • The effectiveness of the ACPUT procedure depends on the experience of programmers preparing the machining templates. 0 INTRODUCTION Nowadays, manufacturing companies must respond quickly to diverse and dynamically changing market needs and expectations. This situation is related to, among other factors, the increasingly common personalization of products, which requires rapid responses to changes in demand in terms of volume and assortment while maintaining high product quality and highly efficient production processes [1] to [3]. To meet the expectations of the mass personalization of products, it is necessary to develop systems that allow for the extensive use of various data contained in product designs and their production technologies [4] to [6]. Such systems,including functions ensuring efficient data exchange between departments involved in production preparation, have been a standard in mass personalization-oriented companies for many years [7] and [8]. In addition, designers and technicians can use tools that make it possible to use the knowledge gained in previously implemented projects (i.e., knowledge-based engineering (KBE)) [9] and [10]. Examples of this are found in the automotive sector, household appliances, sanitary and electronic products, and other industry sectors where similar components with the same purpose may differ due to different vehicle variants. One of the stages to prepare for product manufacture is the design and implementation of special technological tooling, which complements the standard equipment of production machines and equipment and is essential for manufacturing products whose design specifics need to be considered. Due to their intended use, universal (general purpose) and special (dedicated to specific machines, processes, and products) equipment can be distinguished [10] to [12]. The production of special technological tooling is an example of custom-made production, asmake-to-order (MTO) and engineering to order (ETO), in which the design of the device is based on the product model provided by the customer. The process of production special technological tooling is carried out in stages and includes (Fig.1): 1. Analysis of the three-dimensional (3D) CAD model of the product provided by the customer. 2. Development of a tooling model in the 3D CAD program adapted to the product’s shape. 3. Export of designed 3D CAD models of tooling to the CAM program. 4. Development of machining programs in CAM for individual parts of the tooling. 5. Manufacture of tooling parts on a computer numerical control (CNC) machine. 6. Tooling assembly and delivery to the customer. This process is time-consuming and quite often inefficient (in the case of manufacturing groups of products) because despite containing similar manufacturing actions, it must be carried out for each unique product model separately [13]. Using previously acquired engineering knowledge, advanced parametric design methods in CAD systems make it possible to create special models, known as “generative models” in the literature. Such models enable the automation of the operations in Stages 1 and 2 (Fig. 1) related to the development of the tooling design. Based on this approach in a small Polish company (MK-Tech Company), designing and producing special equipment for advanced technical companies from the automotive industry, a procedure for creating machining templates in the CAM system - Automatic CAM Programming Using Machining Templates (ACPUT) [14] was developed, which, in conjunction with a machining knowledge base, makes it possibleto automate the operations in stage 4 (Fig. 1). Since the ACPUT method is limited to a specified product group, the rules it creates are effective and efficient. The templates can be developed using any integrated CAD/CAM class software that allows the parametric construction of CAD models and programming in a scripting language (e.g., Visual Basic for applications (VBA).Sincemachine programming in CAM systems is a time-consuming and high-cost process (it requires the involvement of highly qualified engineering staff), the aimof the ACPUT was to reduce time and minimize human participation in the programming of CNC machines (in the manufactureof special production equipment). This work aims to describe the basic properties of ACPUT and to present results of its testing on effectiveness and efficiency in relation to traditional programming of CNC machines.It should be emphasized that ACPUT is theprocedure of implementation of CAM programming automation, not a specific information system (IT) tool, so the essence of the tests was to compare it with the traditional programming approach (realizedby technicians/CAM programmers), who can use any tools and methods in their work, evenfeature recognition (FR) [15] or well-known group technology (GT) assumptions [16]. 1 PROGRAMMING IN CAM SYSTEMS Generally, regardless of the type of CAM software used, programming in CAM systems can be divided into tasks relating to: 1. the definition of the workpiece shape, 2. the determination of subsequent machining operations, 3. the simulation and verification of the machining program. In the beginning, the general geometric features are defined (i.e., the shape of the workpiece and the shape of the blank from which the part will be made). The user also defines his coordinate system with the zero position so that it is possible to orient the workpiece in the working space of the CNC machine. The programming process involves determining the course of the machining process, depending on the complexity of the workpiece and its size. In individual machining operations, the technological engineer determines: • the specific geometric elements of the CAD model for processing (planes, edges, points), • the machining tool type and features, • the machining strategy determining the machining tool path, Fig. 1. Example of special production tooling manufacture according to ETO [14] Kowalski, M. – Zawadzki, P. – Hamrol, A. • the technological parameters of the operation (speeds and the input/output paths of the machining tool relative to the workpiece). All the settings must undergo initial verification and evaluation in the CAM program (in order to detect possible errors at the operational level). Then, the tool path for the specific operation is generated and saved in the part’s program structure. This process is repeated separately for each operation, andthe geometric changes resulting from previously generated operations are considered. Finally, the whole process is simulated, and if the program requires corrections, the necessary changes are made in the selected operations. After verification, using a special translator, the so-called postprocessor creates a numerical control (NC) code that will be implemented on a specific machine tool. The level of effectiveness of programming CAM depends on the opportunity to develop dedicated solutions to support and automation presented above sequence of activities. Most often, it is carried out using specific tools of the given CAM program (most recent systems offersuch functions). The most popular functions are based on recognizing the characteristic features of a CAD model FR and assigning appropriate machining cycles to them [17]. In practice, these tools can accelerate the work on program preparation but are only effective for models with relatively simple shapes. In the case of advanced surface models (such as the forms with shaping cavities described in this article), the proportion of automatic recognition of geometric features decreases, and the “manual” work of the technician increases to a large extent. These tools are, therefore, a hybrid solution that works well in a single case. Therefore, for a group of families of similar products (several to several dozen items), their use does not significantly affect the effectiveness of the CAM programming process. Although the FR technology has been known for three decades, it remains the subject of various works [18]. The authors focus on the development of various algorithms that can more accurately analyse 3D models and more effectively indicate possible technological operations [19] and [20]. In turn, Zhou et al. [21] presented the FR method (supported by deep learning) for the selection of cutting tools, increasing the effectiveness and efficiency of this task. Another example is the use of FR for spot welding recognition. It should be noted that these works do not deal with the issue of the automation of CAM programming in a general perspective, which can actually improve the efficiency of this process, but only develops algorithms for searching for specific geometric features of 3D models. Moreover, analysing the literature relating to the programming of CNC machines, it can be seen that not much attention has beenpaid to methods forautomating of CAM programming process. Most recent research mainly concerns the improvement of the manufacturing process itself.An example would be the study by Andrankaja et al. [22], in which the decision support method in CAD/CAM design is described based on analysis of the data recorded in the NC code. In the opinion of Zhou and Wu [23], an important problem is the exchange of data between different CAD and CAM systems. They focused their attention on developing a method supporting a data exchange between various CAD/CAM systems, based on the “stl” universal data format. This means that their studyconcerns the third stage of programming related to data export within the process. De Lacalle et al. [24] and [25] have presented methods for improving the CAM generation process in the production of special tooling (forging dies and injection moulds) according to high-speed milling (HSM) requirements. The main objective was to achieve a good surface quality directly from machining, without any additional, tedious, manual work. The authors have achieved the goal using a special postprocessor coded in C language. Once the CAM user had to define (on the CAD model) the theoretical boundaries of the tempered areas, the insert blocks, or the deposition material areas, the CNC program was changed automatically. According to Zahid et al. [26], the effectiveness of NC program preparation is largely dependent on the experience of the user who manually prepares the machining plan. This author developed a special tool for recording, analysing, and improving the code during programming. As a result, both the planning time for machining and its complexity were reduced. Similar studies were presented by Deja and Siemiatkowski [27]. In turn, Tan and Ismail [28] described the methodology for recognizing the features of the CAD model, based on which appropriate operations in the CAM program are selected. Similar considerations were described in papers [29] to [31], presenting different approaches to the data exchange between the CAD and CAM programs, as well as to the recognition of the features of the objects processed, using the universal STEP files format. In all the studies cited above, the CNC machine programming process had a low level of automation. The methods presented wereusually testedon Effectiveness of Automatic CAM Programming Using Machining Templates for the Manufacture of Special Production Tooling simple examples, which makes it difficult to assess their usefulness in practice in industry, where the manufactured parts very often have complicated shapes, and the machining process must not only include shaping, but also surface treatment. The automation of CNC machine programming is discussed in the study by Xu et al. [32]. They presented a method forautomatically generating machining tool paths, based on the data in the integrated CAD/CAPP/ CAM system. Recording the technological knowledge in the CAPPsystem and its further implementation in the CAM system was undertaken by Wang et al. [33]. In their study, Li et al. [34] developed a method that allows the shortening of machining planning time, demonstrating the relationship between machining data and the models used in the manufacturing process. Ma et al. [35] presented a similar approach in their study. In some studies, methods based on artificial intelligence were discussed [36] and [37], although automation in these cases concerned rather the tasks relating to the process of machining planning, and not merely program generation. Kumar [38] presented an original system in which the automation of CNC code generation eliminated the need for specialist human knowledge and minimized the time required tocreate the program. Appropriate recording of technological knowledge wasimportant here, not only because of the quality of the tooling produced but also the quality of the CNC machine programming process in the CAM program. Li [39] provided an overview of the methods used in the recording of the technological knowledge used to prepare the machining process. Although he emphasized the importance of knowledge and the need to write it into the work, he did not specify a universal solution. It can be concluded that the selection of a tool for recording technological knowledge should be adapted to the case in question. More examples on this subject can also be found in [19] and [40] to [44]. In the literature of the subject, no reports confirm the effectiveness of the implementation of automation of CAM programming process in the case of manufacturing a group of products. Therefore, it can be assumed that systems dealing with such cases are not implemented at all or are prepared on an ad hoc basis. The ACPUT methodology fills this gap. 2 PROGRAMMING CAM WITH THE USE OF ACPUT The basis of ACPUT [12] is the development of a special machining template that represents all the technological operations possible for a given group of products. Data and information needed to prepare such a template are stored in the special knowledge base. Knowledge acquired mainly from specialists in a given field is accumulated and written formally, so as to be understood by the computer program. To ensure the correct operation of the machining template, and thus the automation of the preparation process in the CAM environment, 3D models for the tooling must be properly described in the CAD program (i.e., categorization of features and their assignationto the model). Based on this description, the template later automatically selects the appropriate features for the given part. ACPUT is presented in the form of a procedure and includes the following steps (Fig.2): 1. Analysis of 3D CAD models of a given group of tooling parts. 2. Preparation of the technological knowledge base. 3. Defining the geometry necessary to build the machining template and publishing it. 4. Preparation of machining template. 5. Preparation of machining program for each part of the given group of tooling parts (with simulation in CAM program). 6. Preparation of NC program. Step 1 is an analysis of all the CAD models of tooling (i.e., variants) included in a given group, which means checking the technological design of the individual parts and determining the types of machining operations required. The geometric similarities of the parts should be identified, and any variants determined. Once the objects have been analysed, the next step is a description of the technological process in the form of a knowledge base. In Step 2, the knowledge base is built by breaking the technological knowledge down into basic units (i.e., detailed data), divided into two categories. The metadata of databaseis presented in Table 1. Step 3 introduces modifications into the structure of the CAD models, adding a special description for those geometric elements associated with specific machining operations. It can be considered that such a description is a kind of record of metadata in the CAD model, after which geometric elements can be identified and specific actions performed in them in the CAM program (e.g., automatic connection of selected geometric elements of a given model (surface, line, and point) with a specific machining operation, saved onthe machining template). The advantage of this approach is that regardless of the geometric differences of the described elements in different variants of the parts, they will always be interpreted Kowalski, M. – Zawadzki, P. – Hamrol, A. Fig. 2. ACPUT procedure Table 1. Metadata of manufacturing database Knowledge Database Basic data (general for whole process) Machining operations (gathering for each operation separately) I. Definition of machine tool I. Selection of type of machining operation a) Number of controlled axes II. Choice of machining strategy b) Workspace a) The way the tool moves c) Shifts along individual axes b) Milling direction d) Type of control III. Choice of geometry II. Definition of the workpiece IV. Selection of machining tool a) Workpiece geometry a) Tool Type III. Definition of the stock b) Type of frame a) Stock geometry c) Dimensions b) Position of the stock relative to the workpiece V. Determination of cutting parameters IV. Definition of workpiece position in the machine tool a) Depth of cut a) Position of workpiece coordinate system b) Cutting width b) Orientation of workpiece coordinate system (X axis, Z axis) c) Cutting speed V. Definition of how to mount the blank in the machine tool d) Feed speed a) Fastening geometry VI. Tool path generation VI. Definition of the safe plane a) Location of the safe plane Effectiveness of Automatic CAM Programming Using Machining Templates for the Manufacture of Special Production Tooling in the same way. This is one of the conditions for the correct operation of ACPUT. The next step (Step 4) consists of the use of the data contained in the knowledge base and, specifically, described CAD models to create a machining template in the CAM program for a given type of part. Data entered into the knowledge base, including all the basic data such as machine and workpiece data, as well as describing all possible operations to be performed, along with a definition of the machining parameters, tools, etc., are then added to the CAM template. The CAM template automates the generation of a machining program (Step 5 and Step 6) and only requires the selection of the machined part. The appropriate algorithm in CAM then adapts the machining process to it. The automatically generated process should be simulated, assessing the compliance of the results with the assumptions for the given process. In case of any discrepancies, the knowledge record in the database should be improved. The end of the procedure is the preparation of programs specifically for the selected CNC machine. Executing the ACPUT procedure requires different skills in manufacturing knowledge and CAD/CAM systems operation. It is therefore assumed that Step 1 and 2 are realized by an experienced technical. Step 3 can be carried out by even a novice CAM programmer; however, Step4 requires skills at the expert CAM programmer level. The last two steps can be also realized by novice CAM programmers. 3 ACPUT EFFECTIVENESS TEST 3.1 Research Methodology Preliminary tests of ACPUT [8] showed that proceeding according to the ACPUT procedure leads to achieving an effective machining program, meaning that it is compliant with the requirements, according to the manufacturing sheet (ACPUT methodology does not concern optimizing manufacturing program). However, the question arises whether ACPUT is equally efficient if used in everyday industrial practice. To answer this question, a comparative study of the programming efficiency of machining selected group of production tools by operators with various experience (using any tools) was conducted. For comparison of ACPUT efficiency to the traditional way of programming a test was performed. The test was carried out with 10 CAM programmers, five of whom were experts with several years of experience and five were beginners with several months of experience. The following programming outcomes were compared in the analysis: 1. the time needed to prepare the machining programs for individual tooling part Ti [min], 2. cumulative machining time for preparing all tooling groups Tc [min], 3. sum of the cost of programming and the cost of machiningof eachtoolCm [EUR]. Additionally, the tests were madeas to whether the parts machined in accordance with the prepared programs meet the quality requirements (geometric accuracy and surface roughness). The following activities were considered by measuring the programming time [Ti]: • for the first part – becoming acquainted with the whole family of products; detailed familiarization with the first part of the family; becoming acquainted with the manufacturing processsheet for the first part of the family, direct time to prepare the program for the first part in the CAM program (experts and beginners)/preparation time for the machining template (ACPUT procedure), • for each subsequent part of the family - becoming acquainted with the nth part of the family, getting to know the manufacturing process sheet for the nth part of the family; direct program preparation time for the nth part of the family in the CAM program (experts and beginner)/starting the machining template for the nth part (ACPUT procedure). Tooling for plastic pipes assembly, consisting of 50 parts (Fig. 3 – presents only part of them)and manufactured by milling on a CNC machine, was selected for testing. Acting in accordance with the assumptions of ETO (the tooling model was provided by the customer), it was assumed that when preparing programs, it was not possible to interfere with the geometry of the 3D models of the tools, and the files were saved in the universal STEP file format. Fig. 3. Part of the group of special tooling - assembly equipment for plastic pipes In accordance with the ACPUT procedure, the different geometric elements of each tool in the group Kowalski, M. – Zawadzki, P. – Hamrol, A. were first recognized (e.g., Fig. 4), and then described by machining operations, for which the detailed course of the machining process was recorded in the knowledge base. Based on that, the manufacturing process sheets were prepared, consisting of detailed data about machining for each part. All those initial steps (Steps 1 and 2) were made by a specialist (technician). called publications in CATIA V5. Publications are a description enabling the program to refer to the objects they concern. They can be considered a kind of metadata (or tags), after which geometric elements can be identified and other actions performed on them. In the case described, the publications made it possible, for example, to make a later automatic connection of selected geometric elements of a given model (surface, line, and point) with a specific machining operation, saved in the machining template. The advantage of this approach is that, regardless of the geometric differences of the published elements in different variants of the parts, they will always be interpreted in the same way in the program. This is necessary to ensure the correct operation of the automated method described. The list of publications is added to the structure of the given CAD model. Having properly described the 3D models and the knowledge base for the machining process, a CAM process template was prepared in the CATIA V5 program. Data stored in the knowledge base was rewritten to the template, including basic machine and workpiece data, and those describing all the possible operations to be carried out along with a definition of Fig. 5. Process of programming CNC machine with automatic solution using ACPUT procedure Effectiveness of Automatic CAM Programming Using Machining Templates for the Manufacture of Special Production Tooling the machining parameters, tools, etc. The use of the template makes it possible to automate the generation of the machiningprogram, requiring only an indication of the workpiece variant. The appropriate algorithm in the CAM program then adapts the machining process to it. All the stages in the development of the machining program usingthe automatic method areshown in Fig.5. 3.2 Results and Discussion Machining programs were developed for each of the 50 parts according to: a. the ACPUT procedure b. the standard procedure: • by beginner CAM programmers, • by expert CAM programmers. The manufacturing process sheets, developed by a technician in Step 2 of ACPUT, were also made available to the programmers (beginners and experts) so that the data forthe machining process were the same in all cases. In order to verify the surface quality and the accuracy of execution, one variant of the part was created according to the automatic program (ACPUT method) along with programs developed traditionally by an expert and a novice programmer. The machining quality obtained in all three cases was similar (the differences did not exceed the demands and tolerances assumed in the technological card). This is because in each case, it was based on a common data source: a knowledge base describing how a given Table 2. Comparison of preparation time for machining programs part should be made. The differences between the programs (ACPUT procedure/expert programmers/ beginner programmers) mainly concerned the use of various functions of the CAM program to perform specific operations and the settings of the individual treatments, which in the analysed case did not affect the accuracy of the part. 3.2.1 Program Preparation Time The preparation times of the machining programs for all three cases are presented in Table 2. The automatic programming according to ACPUTrequires developing the template (Step 4 in Fig. 2); therefore, the preparation time for the first part was much longer in this case than the preparation times for the subsequent parts. Adifference can also be seen in the case of the programmers (both expert and beginner), programming in the traditional manner, but it not as striking as in the ACPUT case. The differences between the programming time of an expert and beginner programmers are related to their different proficiency in using the CAM program. The total time of the beginner programmers amounts to 2018 min (226 % of the ACPUT time), while the experts had a working time of 1175 min (132 % of ACPUT). The results presented in Table 2 are also shown in Fig. 6. This shows that the ACPUT procedure is already justified when the group of parts is greater than four. CAM preparation time [min] Part No. ACPUT procedure Standard procedure – Beginners (average for 5 participants) Standard procedure - Experts (average for 5 participants) Ti rising Ti rising Range Ti rising Range 1 224 224 145 145 43 79 79 17 2 12 236 58 203 7 28 107 11 3 14 250 42 245 10 29 136 6 4 11 261 42 287 21 18 154 8 5 13 274 41 328 15 32 186 7 6 11 285 35 363 8 25 211 3 7 12 297 39 402 18 28 240 13 8 10 307 44 445 9 20 260 8 9 13 320 40 485 14 27 287 11 10 10 330 37 522 19 22 309 6 … ... ... … … … … … 20 11 460 38 914 7 18 519 3 30 14 606 29 1286 9 16 727 11 40 19 752 41 1673 15 26 955 19 50 10 891 30 2018 14 18 1175 7 482 Kowalski, M. – Zawadzki, P. – Hamrol, A. Fig. 6. CAM preparation time -rising A summary of the machining times from procedure and the beginner programmers amounted to the simulation for each part separately and rising 5.3 %. This mainly resulted from the selection of other are presented in Table 3 and Fig. 7. Because the speeds and the tool’s path of movement during its parameters and the method of processing were approach and departure from the workpiece. It should specified in the technological knowledge base, and be emphasized here that these parameters were not each of the programmers operated using the same specified in the technological knowledge base. data, the differences in the machining times for The total time for the preparation of the individual parts were insignificant. The difference machining programs and the machining for all 50 parts between the ACPUT procedure and the expert was 4863 min in the case of the programming using programmers was 3.3 %, while between the ACPUT ACPUT procedure, 6200 min (representing 127.5 % Table 3. Part machining time on CNC machine based on simulation in CAM program Machining time [min] Standard procedure – Beginner Standard procedure – Expert ACPUT procedure Part No. (average for 5 participants) (average for 5 participants) for each Tc for each Tc for each Tc 1 141 141 147 147 126 126 2 87 228 86 233 94 220 3 119 347 127 360 123 343 4 43 390 47 407 44 387 5 77 467 80 486 78 465 6 42 509 46 533 44 509 7 86 595 90 622 85 594 8 43 638 47 669 45 639 9 99 737 106 775 98 737 10 45 782 49 824 44 780 … ... ... ... ... ... ... 20 82 1800 85 1885 83 1826 30 51 2610 55 2740 51 2673 40 59 3292 58 3471 66 3408 50 49 3972 52 4182 49 4102 Effectiveness of Automatic CAM Programming Using Machining Templates for the Manufacture of Special Production Tooling 483 Fig. 7. Machining time - rising of the working time of the ACPUT procedure) for the beginner technicians and 5277 min (108.5 % of the working time of the automatic solution) for the expert technicians. The above data are presented in Table 4. 3.2.2 Manufacturing Costs The average cost for the machine programming for a single part (MPc) for a given number of parts in the group (Gs), where (Pr) is a pay rate for the CAM programmer/technician is: Table 4. Total manufacturing time MPc = (STi / 60 ×Pr) /Gs. (1) Supposing the pay rate for the beginner programmer (Prb) was € 60/h and the expert’s pay rate (Pre) was € 80/h, it emerged that the average cost for the machine programming for a single part (MPc) from a given group of parts (Gs), was respectively, € 40 for the beginner and € 31 for the expert. In the case of the ACPUT procedure, to build a machining template, expert knowledge is necessary, while to program the machining of subsequent parts, the knowledge of a beginner programmer is sufficient. Thus: CAM preparation and machining time [min] Part No. ACPUT procedure Standard procedure – Beginner (average for 5 participants) Standard procedure – Expert (average for 5 participants) for each Tc for each Tc for each Tc 1 365 365 292 292 205 205 2 99 464 144 436 123 328 3 133 597 169 605 151 479 4 54 651 89 694 62 541 5 90 741 121 815 109 651 6 53 794 81 896 69 720 7 98 892 128 1024 113 833 8 53 945 90 1114 66 899 9 112 1057 146 1260 125 1024 10 55 1112 86 1347 65 1089 … … … … … … … 20 93 2260 122 2798 101 2344 30 65 3216 83 4026 67 3400 40 78 4044 99 5145 92 4363 50 59 4863 82 6200 67 5277 484 Kowalski, M. – Zawadzki, P. – Hamrol, A. MPc = [T1/60× Pre+ (S (Ti-T1))/60× Prb]/ Gs. (2) In the case above, the first part of the procedure assumed a man-hour cost of € 80/h while for the next it totalled € 60/h. It follows that for the ACPUT procedure, the average programming cost for machining a single part was € 19. Assuming a pay rate of € 35/h, the average cost of the machining for a single part in ACPUT procedure was € 46, by beginner programmer (average) € 49 and for the expert programmer € 48. The individual cost of tool manufacture Cm [EUR], consisting of the cost of preparing the machining programs (MPc) and the cost of machining itself, was the lowest for the ACPUT procedure - an average of €66 per part (€ 3300 for all 50 parts). In the case of the beginner technicians, the Cm was € 89 (135.8% of ACPUT Cm, € 4450 for all 50 parts) and € 79 (120.6% 35.8% of ACPUT Cm, € 3950 for all 50 parts) for the experts. A summary of Cm is presented in Fig. 8. Fig. 8. Special production tooling manufacture costs 4 SUMMARY AND CONCLUSIONS In the present paper, a CAM programming procedure, called ACPUT, was presented. ACPUT is dedicated to the manufacture of special technological tooling, which consists of technologically similar parts. The procedure includes the development of special machining templates in the CAM program, supported by the technological knowledge gathered in a specially prepared database. The theoretical functionality of ACPUT was studied in earlier work, but it was necessary to validate its practical effectiveness, taking time and cost of manufacturing the group of tooling into account. Therefore, the main aim of this research was to check when the automatic CAM programming method would be better than the traditional CAM programming approach. Testing under industrial conditions showed that the ACPUT procedure makes it possible to reduce the time needed to develop a machining program. This has a positive effect on the total cost of tooling production. The effectiveness of ACPUTwas tested based on the analysis of the time needed to prepare the CNC program and the machining operation time itself (based on the simulation in CAM). The preparation time of the program is a direct (next to the quality conditions) indicator of the effectiveness of ACPUT. Analysing Table 2 and Fig. 6, it can be seen that the time benefits of using an automatic solution are significant, especially when compared to a less experienced technician. It should be emphasized that the effectiveness of the ACPUT procedure depends on the experience of those who prepare the templates. For the tests Effectiveness of Automatic CAM Programming Using Machining Templates for the Manufacture of Special Production Tooling carried out, ten CAM programmers were invited to participate: five with several years’ experience and five with limited experience (working in this position for only a few months). The first group was considered an expert and the second asa beginner. The aim of the study was not to indicate the predictable differences between them but to evaluate ACPUT which constituted a reference. Separating the technicians into more experienced and less experienced groups had one more purpose: to assess whether, after preparing the machining templates according to ACPUT, someone with less experience would be able to use the automatic solution successfully. The study revealed that it was possible: the results obtained using the automatic solution was even better than the work produced by expert. The ACPUT procedure is, in its assumption, universal; it does not require the use of any specific CAM system or knowledge base construction program, because it does not indicate the use of specific tools to prepare the machining templates. In this study, the CATIA V5 program was used to build the machining template, but it should be considered an example of the possible software that can be used. Machining templates can also be built in other systems of this type. However, it is worth emphasizing that it is best to use integrated CAD/CAM software for these purposes (thereby facilitating data exchange). However, attention should be paid to certain limitations related to the practical application of the proposed method. Firstly, the automation of CNC machine programming according to the ACPUT method assumes the preparation of machining templates for a given family of parts. Their development is, of course, possible in advanced CAD/CAM programs. However, it requires skills in the field of VB programming in order to develop algorithms that allow for automatic recognition of specific geometric features of the machined part and appropriate technological operations in CAM to be assigned to them. In addition, the implementation of programming automation according to ACPUT requires the development and description of standards for the technology used within production companies. For the correct, automatic generation of machining programs in the future, it is advisable to create a knowledge base dependent on the experience of the employees and the analysis of archival works. The achieved results are satisfying, and the conclusions seem useful for practical use. The automation of CAM programming is laborious, complicated and, therefore, difficult to implement in practice. In many cases, the automation of CAM programming does not achieve the assumed results (e.g., the program preparation time gain is not significant) because it is difficult to evaluate the labour intensity for the whole group of manufacturing tools. Research of the ACPUT method complements the knowledge in this field. In terms of further research, it would be worth checkingthe possibility of generating programs for specific types of CNC machine controls. However, in the meantime, more immediate research will focus on the development of a method for the rapid assessment of the technological similarity of the parts for which the preparation of a machining template is planned. It is expected that if the difference between the parts is too great, it will not be profitable to prepare the template and apply the ACPUT procedure. In such a case, it would be preferable to use the traditional programming procedure. 5 ACKNOWLEDGEMENTS We thank the all technology team from Mk-Tech Company for providing a special tooling project for our research, for sharing knowledge about holding milling processes and for helping us during the tests. We also thank Radoslaw Paszkiewicz from Poznan University of Technology for valuable tips and tricks used in CAM programming in the Catia system. 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Received revised form: 2021-08-17 DOI:10.5545/sv-jme.2021.7284 Original Scientific Paper Accepted for publication: 2021-09-09 Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network Yang, S. – Luo, X. – Li, C. Shuai Yang1,* – Xing Luo2 – Chuan Li2 1 Chongqing Technology and Business University, National Research Base of Intelligent Manufacturing Service, China 2 Chongqing Technology and Business University, School of Management Science and Engineering, China As a key component of a mechanical drive system, the failure of the reducer will usually cause huge economic losses and even lead to serious casualties in extreme cases. To solve this problem, a two-dimensional convolutional neural network (2D-CNN) is proposed for the fault diagnosis of the rotation vector (RV) reducer installed on the industrial robot (IR). The proposed method can automatically extract the features from the data and reduce the connections between neurons and the parameters that need to be trained with its local receptive field, weight sharing, and subsampling features. Due to the aforementioned characteristics, the efficiency of network training is significantly improved, and verified by the experimental simulations. Comparative experiments with other mainstream methods are carried out to further validate the fault classification accuracy of the proposed method. The results indicate that the proposed method out-performs all the selected methods. Keywords: fault diagnosis, convolutional neural network, RV reducer Highlights • An improved method based on deep-learning algorithms is proposed in this paper. • 2D-FFT and 2D-CNN are combined for the fault diagnosis. • The efficiency of network training is significantly improved. • Comparative experiments with other mainstream methods are carried out, and the fault classification accuracy of the proposed method is validated. 0 INTRODUCTION An industrial robot (IR) is defined as a device with multi-joint manipulator or multi-degree of freedom, which has been widely used in various industries, such as electronics, manufacturing, and aerospace [1]. Due to its automaticity and controllability, IR can provide a variety of functions in industrial processing or manufacturing. In the late 1950s, the first-generation IR was designed and manufactured by General Motors for completing simple and repetitive operations. After several decades of evolution, IR has become one of the most common devices in the manufacturing sector. With the development of manufacturing and computer science, the IR has been increasing in intelligence, flexibility, and interactivity, which can satisfy the diverse needs of various costumers [2]. For most industrial robots, there are three major components and six subsystems. Among them, the driving system (motor) is the key part that provides power to the mechanical system of an IR [3]. Due to the high rotation speed and torque of the servo motor, a motor reducer is required for the driving system, which can match the rotation speed and transmit the torque between driving motor and actuator. The accuracy of the transmission ratio for a reducer directly affects the displacementprecision and reduces the service life of the IR [4]. Because of the harsh working conditions, failure of the reducer is one of the most common phenomena in a mechanical system. Wear and leakage are two major types of failure, which can cause the shut-down of a mechanical system. Therefore, fault detection and diagnosis of the reducer are required. In recent years, the deep-learning (DL) method has become the dominant method in fault diagnosis for extraction, detection, and classification. The mainstream DLmethods include deep neural network (DNN), deep belief network (DBN), recursive neural network (RNN) and convolutional neural network (CNN). Among them, CNN was initially applied to the recognition and classification of images. However, it also shows promising results in fault diagnosis, which can automatically extract features from the original data without manual selection. By training and adjusting the values of the convolution kernel, CNN can achieve the classification and recognition of fault based on the raw data [5] and [6]. Pan et al. [7] propose an improved bearing fault diagnosis method based on CNN and a long-short-term memory (LSTM) recurrent neural network whose input is the raw sampling signal without any pre­processing or traditional feature extraction. Janssens et al. [8] pre-processed the vibration data of bearing shell by fast Fourier transform (FFT) and input it into CNN for fault detection. Jing et al. [9] focused on the research and development of CNN, which can directly learn features from the frequency data of vibration signals, and test the different performance of feature learning from original data, spectrum, and time-frequency combined data. Azamfar et al. [10] proposed a fault diagnosis method based on the analysis of motor current characteristics. The data obtained from multiple current sensors are fused by a new two-dimensional convolutional neural network structure, which can be directly used for classification without manual feature extraction. DCNN was applied to solve the multi-state fault identification problem of gearboxes. By directly inputting the original signal, a higher diagnosis accuracy is obtained with a lower calculation time cost [11]. The two-dimensional mapping representation method based on cyclic spectrum coherence (CSCoh) and CNN is used to improve the fault identification performance of rolling bearings, which is a new fault diagnosis method based on DLproposed by Chen et al. [12]. Anew full closed-loop method based on a deep convolution neural network is proposed to detect and classify the data of power quality disturbances [13]. Xuan and You [14] proposed a hierarchical convolutional neural network (HCNN) based on DL, which was used to detect pancreatic tumours. This method can improve the performance of the classifier and reduce the cost of the medical Internet of Things (IoMT). A framework based on DL was applied to classify the target data, which adopted deep CNN to learn the compact and effective representation of each signal from wavelet coefficients. This method is used to classify the ultrasonic signals of carbon fibre reinforced polymer (CFRP) specimens with voids and delamination [15]. Lu et al. [16] proposed a new method of rice disease recognition based on a deep convolution neural network and achieved good results. In [17], a time-varying reliability method of rotation vector (RV) reducer for industrial robots with multiple failure modes based on the Kriging model is proposed. Based on the previous research, the application of the CNN model for fault detection and classification has achieved tremendous progress in many fields. However, for RV reducers of IR, the application of the CNN model remains rare. As is well-known, a convolutional neural network has unique advantages in processing massive data and can learn features from massive data. In this paper, a 2D-CNN based on the experimental vibration data after 2D-FFT is proposed for the fault diagnosis of an RV reducer for IR. Comparisons are conducted with other mainstream methods. The results indicate that the proposed method shows better performance in the fault diagnosis of an RV reducer. 1 METHODS 1.1 Back Propagation (BP) Algorithm Since Rumelhart et al. [18] and [19] reinvented and described the application of the BP algorithm in neural networks in 1986, this algorithm has become a standard learning method for neural network models. Due to its high efficiency, the BP algorithm is also selected to determine the network parameters of the CNN model in this paper. With the BP algorithm, the matrix of universal parameters can be optimized by error-oriented motion, which is used for the classified task of the CNN. The residual error between the actual and expected output is sent back to the hidden layer and eventually back to the input layer. Based on the results, the weighted coefficient will be adjusted to minimize the error till the optimal coefficient is obtained. The loss function for a single sample is written as follows: JWbxy˜,;,°.1hWbx.y2 ,˜°,(1) 2 where hW,b(x) is the actual output, y is the desired output, W is the weight-matrix, b is a bias vector. Assume there are m samples in each data set, the overall cost function can be defined as: 1.1˜°i˜°i2. JWb˜,°.m.hWb,˜x°.y ,(2) m1ˆ2 where G is the regularization term or weight decay term, which can reduce the magnitude of the weight to prevent overfitting, m is the number of samples. G can be determined by: n.1s ll ˆs2 l1l .˜°j°1.Wij.(3) .2l°1i°1, . °.1.,. ,2 ln,n2 ll where . stands for the weight decay parameter; Wijl is the weight matrix between the ithneuron in layer l and the jth neuron in layer l +1, nl is the output layer. The gradient descent method updates the parameters w and b: ll. W˜W°ˆ.,, (4) ijnewijold.WlJWb.ijold ll. b˜b°ˆJWb(5) .,, . inewioldl .b iold Yang, S. – Luo, X. – Li, C. where a represents the learning rate; .bilis the bias vector of the ith neuron in layer l. Using the backpropagation algorithm to calculate the partial derivative, the residual calculation formula for each output unit of the output layer is listed as follows: n.12nn lll ˜°nyhx°yafzi,(6) i.Wb,.ˆ..i.iˆ...ˆ l .zi2 nln l where aiis the ith input of output layer; fzi. ˜° is the derivative of the activation function that takes the partial derivative of ainl; .dinlis the residual. The residual calculation formula for each output unit of the llT ˜JW°,b..ˆ.1°.a,(12) l W ˆl.1(13) ˜JWb°,... l b The calculation formula for the change of parameters W and b are as follows: ˜Wl°˜Wl...,, (14) JWbˆ l W ˜bl°˜bl..JWb,.(15) b.ˆ l The final parameters update formulas are as follows: other layer can be written as:  l  ... ˆ.. 1 W l ˜ ll W   W  W . °  ,(16) W ..l m f . .. l z i . (7) ˆ s1 nl j° l ° l ˜ ˜ n i ijj  ...  ˆ.. 1 b lll b  b ˜ °  (17) The calculation method of partial derivative can m be presented by: ˜ ˜W ij l JWbxy,;,al.n,1.2 Typical Structure of CNN Method l°..ii(8) n ˜JWbxy°,;,...il.(9) ˜bil Thus, the residual calculation formula for the output layer and other layer are rewritten as: lll ˜n°.yafzˆ, nˇn(10) ..... lllˆ1l ˜°...WT˜.ˇf.z.(11) .. Calculating the final required partial derivative value: CNN is a feedforward neural network with a deep structure that includes convolution computation [20]. The initial concept of CNN was proposed by LeCun et al. [21]. However, the actual application of CNN began in 2012, due to the appearance of AlexNet [22]. This method has been widely used in various fields, such as image recognition, speech separation and video classification. For most CNN models in fault classification, feature extraction, feature selection, and fault classification are three major parts. As shown in Fig. 1, there are five layers in the structure of CNN: input Fig. 1. Typical structure of CNN Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network layer, convolution layer, pooling layer, fully connected layer, and output layer. (1) Convolution layer: as shown Fig. 1, there are many feature maps in the convolution layer; for each map, multiple neurons are distributed. The feature map of the former layer is connected to those neurons by the convolution kernel. As a feature matrix, the convolution kernel can extract the partial features of the input data. Due to the weight sharing of the convolution layer, the parameters in this layer can be minimized, which will improve the time of computation and avoid overfitting. The output area of the convolution layer can be determined by: Imapcon˜kers+ˇpadding 2 Omap=°1.(18) constride In this equation, Omapcon is the output area of the convolution layer, Imapcon is the input area of convolution layer, kers is the convolution kernel, stride stands for the sliding length of convolution kernel, padding represents the padding size. The specific convolution formula involved in the convolution process is as follows: k.1 lr.  lij˜°,.l˜°jlljljj˜°ˆ˜.ˆ°l y.fwx.bfwx.b,(19) i.i. ii jˆ.0 lj˜ where wi°.is the jth weight matrix of the ith () convolution kernel in layer l; xlrjis the jthconvoluted local region in layer l; k is the width of convolution kernel; is the bias terms in layer l and a vector; f(ˇ) is the activation function. There are five frequently-used activation functions: Sigmoid function, hyperbolic tangent (Tanh) function, rectified linear units (ReLU) function, exponential linear unit (ELU) function and MaxOut function. In this research, the ReLU function is selected as the activation function, because it can solve both explosion and disappearance of the gradient. Meanwhile, the convergence rate of ReLU function is much higher than the sigmoid and tanh functions [23]. The curve diagram of the ReLU function is shown in Fig. 2, which can be written as: xx ˜.° max,° ReLU˜°x.˜0x.,(20) 2 'ˆ0x.0 ReLU˜°x...(21) 1x.0 . (2) Pooling layer: Similar to the convolutional layer, the pooling layer is composed of multiple feature maps. Although the number of feature maps is the same as the former layer, the size of each map is smaller. By pooling, the feature vector will be reduced, which leads to the second-extraction of feature data. In this paper, mean pooling is chosen instead of maximum pooling. The number of output feature maps of the pooling layer in CNN can be determined by: Imappool°kersOmappool˜.1,(22) stride where Omappool is the size of the output feature map of the pooling layer; Imappool is the size of the feature map of the input pool layer. Fig. 2. ReLU graph (3) Fully connected layer: in this layer, each neuron is fully connected with all neurons in the previous layer. The full connection layer can integrate the local information with class discrimination in the output of the pooling layer. The forward propagation in this layer can be represented as: l˜1lll Zj°.nWaiji˜bj.(23) i°1 In the formula, Wijlis the weight matrix between the ithneuron in layer l and the jthneuron in layer l+1; Zlj+1 is the output value of the jth neuron in l+1 layer; .bilis the bias vector of all neurons in layer l to the jth neuron in layer l+1; ailis the output feature maps of l layer. (4) Output layer: The output layer uses softmax logistic regression to classify the input data. Mathematically speaking, the softmax operation can be described as: Yang, S. – Luo, X. – Li, C. ˜°.ezi pz,(24) iNcz ei .1 i. where p(z)i represents the probability that z belongs to category i, i . Nc. 1.3 Advantages of CNN for Fault Classification As mentioned before, CNN can automatically learn the features from the raw data without manual selection. Through training, CNN will optimize the model’s parameters and achieve the recognition and classification of data. Because of this characteristic, the original vibration signal can be directly used as input for CNN, which will bring two benefits. 1) By using the raw data, pre-processing of the data can be avoided, which will save time and maximumly ensure the authenticity of data. 2) For the signal processing of a complex system, the selection of feature functions usually requires rich mechanical mathematical knowledge, which means it is difficult for a common technician to extract effective features. However, data features can be extracted directly by using CNN. The weight sharing of the convolution layer in CNN is the same as sparse connection, which reduces the parameters in the network and lowers the computation time and difficulty. Meanwhile, due to the regularization effect, the stability and generalization ability of network structure are improved, and over-fitting is also avoided [24]. As mentioned before, by pooling layers, the number of neurons in the model will be minimized, and the robustness of translation invariance in input space is improved [25]. Moreover, the structure of CNN is highly extensible, and a deeper layer can be designed in experiments. Therefore, CNN can be used for complex classification due to the ability of fault data extraction. As a symbolic algorithm of deep learning, CNN can extract high-order features from input data. Specifically, the convolutional layer and pooling layer in the CNN can respond to the translation invariance of input features; in other words, they can identify similar features located in different spatial positions. Therefore, the CNN model has become one of the most popular models by researchers for fault identification and classification. Since the data samples from the RV reducer are huge and complex, the CNN model is chosen for the fault identification and classification. 2 FAULT DIAGNOSIS METHOD BASED ON CNN According to the different characteristics of the processing object and the dimension of the input data, CNN is divided into one dimension, two dimensions, and three dimensions, which play a role in different fields. The 2D-CNN weight filters take the dot product of the entire input as they move along the spatial dimension and sum them up. Unlike filters of 1D-CNN, which move in the time dimension, filters of 2D-CNN move in the space dimension, with two dimensions of height and width, which can more accurately and comprehensivelyextract the features of reconstructed data. In this paper, the 2D-CNN model is used to diagnose the fault data of the RV reducer used in an IR, and the vibration signal is directly used as the input to realize end-to-end fault diagnosis. 2.1 The Data Collection In this paper, the RV reducer of an IR (model No. BRTIRUS1510A) is chosen as the research subject. There are six types faults: normal, broken teeth of planetary gear A, pitting of sun gear B, broken teeth of planetary gear B, crack of planetary gear B and broken teeth of planetary gear B respectively. For each fault, the rotation speed of the IR maintains 600 r/min without any load. Meanwhile, the motion track of IR will repeat 20 times, which is fixed (maximum motion range). For both motion and accelerate sensors, the sampling frequency is 100 kHz, the sampling time is 20 seconds, which will repeat 10 times. The experimental rig is shown in Fig. 3. Fig. 3. Experimental device for data collection Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network The experimental design involves six different conditions corresponding to the faults, which includes one healthy mode (NR) and five fault modes (fault 1 to fault 5). Broken teeth are usually divided into fatigue fracture and overload fracture in the reducer, which are caused by fatigue stress and short-term overload or impact load. The pitting of teeth is generated by the teeth with small cracks mesh continuously, which lead to a small piece of metal slides off the tooth surface. The cracks of the teeth in the reducer are mainly Amplitude [V] triggered by machine overloaded, insufficient design strength, and defective material. Fig. 4 shows an example of a vibration signal acquired in each one of the fault types. The fault categories are listed in Table 1. Fig. 5 shows an example of each one of the five types of faults. Fig. 5. Five fault conditions; a) broken tooth in sun gear B, b) pitting in sun gear B, c) broken tooth in planetary gear A, d) cracking in planetary gear B, and e) cracking in planetary gear B Table 1. Fault categories Serial number Fault type Fault location Degree of fault Normal NR None None \ Fault of tooth breakage of the second shaft RV40E-121 Planetary gear A Fault 1 Broken teeth Completely broken teeth planetary gear Fault 2 Pitting Tooth pitting failure of the third shaft RV20E-121 sun gear Moderate Sun gear B Fault 3 Broken teeth Tooth breakage fault of the third shaft RV20E-121 sun gear Completely broken teeth Planetary gear B Crack fault of the third shaft RV20E-121 planetary gear Fault 4 Crack Width 0.5 mm Depth 0.5 mm teeth Fault of tooth breakage of the third shaft RV20E-121 Fault 5 Broken teeth Completely broken teeth planetary gear In this paper, for each fault, there will be 6×1000 samples. Among them, 4200 will be chosen for training, and the rest will be used for verification. 2.2 Experimental Environment The hardware and software settings of this experimental environment are shown in Table 2. Table 2. Development environment Hardware Configuration or installation information Processor Intel(R) Core(TM) i7-6700 CPU @3.40GHZ 3.41GHz RAM 8.0 GB Operating system Windows 10 Professional 64-bit Compilation environment MATLAB R2018a Yang, S. – Luo, X. – Li, C. 2.3 Data Processing There are 6000 samples in this paper, and each sample has 1024 data points (i.e. 1024×1). The model used in this paper is 2D-CNN, so one-dimensional data is changed into two-dimensional data (i.e. 32×32×1) by pre-processing. Then, after 2D-FFT processing, the time domain diagram is changed into the frequency domain diagram, as shown in Fig. 6. The 2D-FFT is usually calculated by the row and column decomposition algorithm; in other words, by using its separability, the two-dimensional FFT is divided into row-oriented FFT and column-oriented FFT and then calculated in sequence. Generally speaking, the row-column sequence of 1D-FFT algorithm has no great influence on the operation results of 2D-FFT, as long as the row-oriented FFTand column-oriented FFTcan be calculated separately. The Fourier transform can transform the signal from time domain to frequency domain, and then study the spectrum structure and variation rule of the signal. Some signals in the time domain have more obvious features in the frequency domain, which enables the CNN model to extract data features more accurately. 2.4 Model Design The CNN model used in this paper is shown in Fig. 7, which consists of an input layer, convolution layer C1, pooling layer P2, convolution layer C3, pooling layer P4, fully connected layer and output layer. The activation function of the convolution layer is a) b) c) d) e) f) Fig. 6. 2D raw data and frequency domain diagram; a) normal, b) fault 1, c) fault 2, d) fault 3, e) fault 4, and f) fault 5 the ReLU function, the pooling layer adopts mean pooling, and the fully connected layer contains 250 processing units. In order to automatically adapt to the different characteristics of vibration signals, the key parameters of CNN need to be optimized. The parameters of the network layer structure, convolution kernel size, number of iterations, learning rate, number of Fig. 7. CNN framework diagram used in this paper Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network Table 3. Parameters of CNN model Layers Types Model parameters Training parameters 2 Convolutional layer Filter width = 5, Filter height = 5, Filter channel = 1, Filter number = 5, Bias = 5 Minibatch size = 200 3 4 5 Pooling layer Convolutional layer Pooling layer Sub-sampling rate = 2 Filter width = 5, Filter height = 5, Filter channel = 5, Filter number = 10, Bias = 10 Sub-sampling rate = 2 Learning rate = 0.01 Momentum = 0.1 Max epochs = 300 Training sample rate = 70 % Activation=’ReLU’ 6 Full-connected layer 250 nodes 7 Softmax classifier 6 outputs convolution kernels, activation function, and pooling method all need to be adjusted. The optimal structure parameters are shown in Table 3. The diagnosis process proposed in this paper is shown in Fig. 8, and the processing steps are listed as follows: Step 1: Collecting vibration signals of different faults from the RV reducer through sensors installed on the machine; Step 2: Pre-process each kind of fault data and divide it into training samples and test samples; Step 3: Carrying out two-dimensional Fourier transform on the data; Step 4: Initializing the 2D-CNN model; Step 5: Fine-tuning the parameters; Step 6: Learning the features of the training samples by 2D-CNN model, and then using the trained model for fault diagnosis of the test samples; Step 7: Output the fault diagnosis results. 2.5 Experimental Results The 2D-CNN model designed in this paper is used to classify six kinds of fault data of robots, and the classification results of test sets are shown in Table 4. Table 4. Fault classification results of test sets Fault category Sample size Accuracy [%] NR 300 99 1 300 100 2 300 99.33 3 300 100 4 300 100 5 300 99.67 Overall accuracy 99.67 Based on the above table, the proposed CNN model in this paper can achieve 99.67 % overall accuracy in recognition rate. For Fault 1, Fault 3, and Fault 4, the recognition rate can reach 100 %. The accuracy of normal data is 99 %, the accuracy of Fault 2 is 99.3 %, and the accuracy of Fault 5 is 99.67 %. It can be seen from Table 4 that the classification accuracy rate of Fault 2 is the lowest, and Fault 2 is pitting. As shown in Figure 4, the vibration signal of Fault 2 is quite different from the other four fault signals. All the results indicate that the CNN model is an effective method for fault classification of a reducer on an industrial robot (IR). 3 SIMULATION COMPARISON To further verify the efficiency of the proposed method, simulations of comparison are carried out in this paper. Four mainstream methods are chosen as comparative subjects, which are 1D-CNN, DBN, Stacked AutoEncoder (SAE), RNN and Neural Network (NN), respectively. Yang, S. – Luo, X. – Li, C. Similar to 2D-CNN, 1D-CNN can automatically extract feature data through weight sharing to avoid overfitting. Table 5 shows the parameters of the 1D-CNN model used in this paper. Table 5. Parameters of 1D-CNN model Parameter Value Con1 Omaps = 5; kers = 15; actv = ’sigm’ Pool1,2 Scale = 5; pool = ’mean’ Con2 Omaps = 5; kers = 13; actv = ’sigm’ Numepochs 500 Batchsize 50 Learning rate 0.015 Output layer Softmax DBN is a probabilistic generative model, which is formed by stacking several restricted Boltzmann machines (RBM) [26]. Its training process is to use unsupervised greedy layer-wise pre-train to obtain weights. The bottom layer is mainly used to receive the input data and transform the input data into a hidden layer through RBM, that is, the input of higher layer RBM comes from the output of lower layer RBM. Stacked RBM networks form a DBN model to extract the features of fault data, and then the classifier is used for classification. Table 6 shows the parameters of the DBN model used in this paper. Table 6. Parameters of DBN model Parameter Value Dbnsizes 10×10 Learning rate 0.000003 Numepochs 500 Batchsize 100 Momentum 0.00001 Activation function tanh Output layer Softmax SAE is a deep neural network model composed of multi-layer AutoEncoder (AE), and AE is a three-layer unsupervised nonlinear neural network for feature extraction, including input layer, hidden layer, and output layer. The key idea of SAE is to let the network learn the features of input data in an unsupervised way [27]. Firstly, the greedy layer-wise training method is used to train the SAE network unsupervised, and the features of the fault data are obtained. Then, the trained SAE is connected with the softmax layer to classify the fault data of the test set. Table 7 shows the parameters of the SAE model used in this paper. NN is composed of the input layer, hidden layer, and output layer, the latter of which have only one layer, and the number of hidden layers can be arbitrary. The input layer is introduced from the instance feature vectors in the training set, and is transferred to the next layer according to the weights between the connection points. Table 8 shows the parameters of the NN model used in this paper. Table 7. Parameters of SAE model Parameter Value Hidden layer 100 Numepochs 20 Batchsize 200 InputZeroMaskedFraction 0.5 Learning rate 0.0026 Activation function tanh Output layer Softmax Table 8. Parameters of NN model Parameter Value Hidden layer 100 Numepochs 500 Batchsize 100 Momentum 0.5 Learning rate 0.21 Scaling_learningrate 1 Activation function sigm Output layer Logistic Six types of RV reducer data (one normal data and five fault) are collected in our experiments. The diagnosis results are shown in Table 9 and Fig. 9. The fault diagnosis accuracy of five tests is shown in Fig. 9. It can be seen from the figure that the accuracy of fault diagnosis results of the 2D-CNN model is the highest, with the highest being 100 % and the lowest being 99.33 %, followed by 1D-CNN, NN and SAE. DBN performed the worst in these models. Table 9 shows the average classification accuracy of the above five methods. In the classification performance, the fault classification method based on 2D-CNN has achieved better classification performance than other deep networks, and its average accuracy can reach 99.67 %. 1D-CNN’s classification effect is ranked after 2D-CNN, and its average classification accuracy is 98.46 %. NN’s classification effect is inferior to that of 1D-CNN, and its average classification accuracy is 83.47 %. The classification performance of SAE based on AEs is general, and the average accuracy of SAE is 83.05 %. The classification accuracy of DBN based on RBMs is low, and the average classification accuracy of DBN is only 58.08 %. To summarize, the 2D-CNN-based classification method has the highest average Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network Accuracy [%] Fig. 9. Comparison of model accuracy accuracy among the five methods, which is 1.21 % higher than 1D-CNN, 16.2 % higher than NN, 41.59 % higher than DBN and 16.62 % higher than SAE. Table 9. Mean accuracy of model Model Mean accuracy [%] 2D-CNN 99.67 1D-CNN 98.46 DBN 58.08 NN 83.47 SAE 83.05 4 CONCLUSION As a key component of industrial robots, the RV reducer often has various faults due to its bad operating environment. The failure of the reducer can bring huge economic losses to a company and even lead to serious casualties in some extreme cases. Therefore, the fault detection and diagnosis of the reducer is very important. In order to solve this problem, a 2D-CNN based on the experimental vibration data after 2D-FFT is proposed for the fault diagnosis of RV gear for IR. In the proposed algorithm, the time-frequency transformation of the original data is firstly performed by the 2D-FFT. Secondly, the convolutional layer and pooling layer of 2D-CNN are used to extract the fault features contained in the data. Finally, the Softmax layer classifies the extracted features. 2D-CNN can automatically learn the features in the data, and it has three features: 1) The spatial connection of data is local. Each neuron does not need to perform convolution computation on all the data. The global information can be obtained by integrating different local neurons obtained by convolution computation at a higher level, which can reduce the number of connections; 2) The weight sharing between different neurons can reduce the parameters that need to be solved, and multiple feature maps can be obtained by using multiple filters to convolute the input data; 3) Subsampling can reduce the amount of data to be processed while retaining useful information. These features are conducive to improving the efficiency of network training. The proposed method is applied to the rotation vector reducer for industrial robots. The data used in this paper include one normal data and five fault data, and the diagnosis accuracy reaches 99.67 %. However, the comparison models include 1D-CNN, DBN, NN, SAE. And their accuracy rates are 98.46 %, 58.08 %, 83.47 %, 83.05 %, respectively. Experimental results show that, compared with other peer models, the proposed model has good fault diagnosis accuracy. Yang, S. – Luo, X. – Li, C. 5 ACKNOWLEDGMENTS This research is supported by National Natural Science Foundation of China (51905058), Natural Science Foundation of Chongqing (cstc2020jcyj­msxmX0182, cstc2019jcyj-zdxmX0013), Key Project of Technology Innovation and Application of Chongqing (cstc2019jscx-fxydX0077), Research Start-Up Funds of Chongqing Technology and Business University (1856018), Key Research Platform Project of Chongqing Technology and Business University (ZDPTTD201918). 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[26] Xu, F., Fang, Z., Tang, R., Li, X.,Tsui, K.L. (2020). An unsupervised and enhanced deep belief network for bearing performance degradation assessment. Measurement, vol. 162, art. ID 107902, DOI:10.1016/j.measurement.2020.107902. [27] Vincent, P., Larochelle, H., Lajoie, I, Bengio, Y., Manzagol, P.­ A. (2010). Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, vol. 11, p. 3371-3408. Yang, S. – Luo, X. – Li, C. Strojniški vestnik - Journal of Mechanical Engineering 67(2021)10, 501-515 Received for review: 2021-06-24 Š 2021 Journal of Mechanical Engineering. All rights reserved. Received revised form: 2021-09-05 DOI:10.5545/sv-jme.2021.7300 Original Scientific Paper Accepted for publication: 2021-09-21 Response Analysis of a Scraper Conveyor under Chain Faults Based on MBD-DEM-FEM Wang, Z. – Li, B. – Liang, C. – Wang, X. – Li, J. Zisheng Wang1,2 – Bo Li1,2 – Chao Liang1,2 – Xuewen Wang1,2,* – Jiahao Li1,2 1 Taiyuan University of Technology, College of Mechanical and Vehicle Engineering, China 2 Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment, China To tackle the difficulty in obtaining the response data of chain and bulk coal under chain faults, this paper uses a new method for the fault simulation of scraper chains based on the coupling of multi-body dynamics (MBD), discrete element method (DEM), and finite element method (FEM). With the force and stacking angle as response values, the contact parameters of bulk coal were revised using a rotary transport test. The simplified DEM-MBD model was verified from the resistance using the point-by-point method. The static structure model of the chain was verified by the chain tensile experiment. The DEM-MBD coupling results show that when the chain is stuck or broken, the dynamic properties of the chain and bulk coal fluctuate sharply, and the wear of the medium plate increases. Based on the DEM-MBD coupling results and the DEM-FEM unidirectional coupling, the stress, strain and life were acquired, and were verified experimentally. Regarding the fracture, the Plackett-Burman test was used to determine that the crack depth, initial angle, and tensile load significantly affect the stress intensity factor (SIF). The quadratic model between significant factors and SIF was constructed using the response surface method, which provides a reference for the simulation of the scraper conveyor, the fault mechanism, and the optimization of the design of the chain. Keywords: multi-body dynamics, discrete element method, finite element method, scraper chain fault, stress intensity factor, response surface method Highlights • A method for the fault simulation of a scraper conveyor based on the MBD-DEM-FEM was proposed. • The contact parameters of bulk coal were revised from the force and stacking angle using the response surface method. • The response data of the chain and bulk coal under chain faults were obtained. • The interactions between factors on stress intensity factor were analysed using the response surface method. 0 INTRODUCTION As the core structure of the scraper conveyor, the scraper chain is subject to frequent faults, which seriously threatens the safety of workers and the mining efficiency of coal mines [1]. For satisfying the high requirements of intelligent coal mines for scraper chains, it is not feasible to perform fault tests on actual scraper conveyors or scaled models to obtain real-time data. This method is costly, and it is difficult to extract data. Consequently, safety cannot be guaranteed due to the uncertainty of fault. The simulation method based on computer aided design has low cost and high safety factor, and can reproduce the fault state realistically. The common simulation methods include finite element method (FEM), multi-body dynamics (MBD), and discrete element method (DEM). FEM is based on the idea of discretization and numerical approximation to solve problems [2]. Based on the simplified chain transmission model, researchers have obtained the stress, strain, [3] and [4] and fatigue life [5] of chains under different conditions through transient dynamics or static structural analysis. However, the finite element analysis (FEA) based on the simplified model puts higher demands on the boundary conditions. The accuracy of boundary conditions set only by finite element software is not high but can be enhanced by coupling with DEM [6] and MBD. MBD focuses on the kinematic and dynamic behaviour of multi-body systems and the mechanical interaction between parts. The velocity and vibration characteristics of the scraper chain under chain stuck and breaking faults were gained by Jiang et al. [7]. Xie et al. [8] discussed the torsional pendulum and vibration characteristics of the chain under chain stuck fault. With the maturity of flexible technology, scholars have made the chain flexible to obtain the stress and deformation [9] and [10]. Previous studies rarely considered the role of bulk coal or only replaced it with a constant load, ignoring the distribution and time-varying characteristics of bulk coal. However, the force and movement of bulk coal under fault conditions may worsen the fault and accelerate the failure of parts. Considering the discrete characteristics of bulk coal [11], DEM has been applied to research the transport state of scraper conveyors [6] and [12] and the failure of key parts [13] and [14]. Researchers have improved the reliability of simulation by modifying parameters [15] and [16] and coupling simulation. The MBD-DEM bidirectional coupling model of scraper conveyor was established by Zhang et al. [17] to realize real-time sharing of information between bulk coal and rigid bodies. The surface cracks caused by the synergy of complex factors such as wear, corrosion, and deformation are reasons for the fatigue fracture of the chain [18]. It is necessary to discuss the initiation and propagation of cracks. The stress intensity factor (SIF) is a fracture parameter that characterizes the stress field at the crack front, which is affected by the size and shape of the crack. Xue et al. [19] investigated the effects of crack depth and position on fracture parameters through the single factor test, which does not involve the interaction between the factors controlling the fracture of chains. The response surface method (RSM) is an optimization method combining mathematical statistics and modelling [20]. The effects of interaction on the response value can be investigated. It can be seen that the advantages of each method can compensate for the deficiencies of other methods, so this study used a new method for the fault simulation of a scraper conveyor based on the coupling of MBD-DEM-FEM. First, the MBD-DEM coupling model was established and verified to gain the response characteristics of chain and bulk coal under chain stuck and breaking faults. Combining the above results and DEM-FEM coupling, this study obtained the stress, strain, and life of the chains. For the fracture failure, through single factor testing and response surface testing, the single and interactive effects of crack depth, initial angle, and tensile load on the SIF were investigated. This provides a reference for the simulation of scraper conveyors, and the fault mechanism and optimization design of the chain. 1 SIMULATION MODELS AND TEST 1.1 Dynamic Model The coupling simulation with the complete length of the scraper conveyor (SGZ880/800) is not workable [17], so the dynamic model is simplified as necessary. When the chain is stuck or broken, the chain far from the fault point will not respond in a short time. Therefore, only three middle troughs, scraper chains, and drive sprockets are retained; the retarder, hydraulic coupler, and drive motor are removed, and the bottom chute for the return of the chains is added. The simplified model is 5 metres long, as shown in Fig. 1. In the pre simulation, the sprockets drove the scraper chains to run smoothly, which satisfied the desired motion characteristics. The kinematic pairs and contacts of components were set up in RecurDyn dynamic software. The stiffness and damping coefficients of contacts between parts were adjusted by pre-simulation to be 600000 Nˇmm–1 and 1000 Nˇ(mm/s)–1. 1.2 Construction and Modification of Bulk Coal 1.2.1 Model of Coal Particle The three typical shapes of coal particles were built by the spherical filling method [21], shown in Fig. 2. In order to obtain the contact characteristics of the bulk coal with the scraper chain and the medium plate more accurately, coal with large (50 mm to 100 mm), medium (25 mm to 50 mm), and small (13 mm to 25 mm) sizes was generated by the particle factory fixed above the machine tail, and the mass proportions of three sizes are 0.25, 0.4, and 0.35. Hertz-Mindlin (no slip) [22] and Hertz-Mindlin with Archard wear [14] were chosen for the contact models between particles and between particles and rigid bodies, respectively. The intrinsic parameters Fig. 1. The mechanical relationship of simplified model; a) simplified model; and b) mechanical relationship measured by the group members through the designed experiments are listed in Table 1 [16]. Fig. 2. The shapes and mass proportions of particles Table 1. The intrinsic parameters Parameters Steel Coal Shear modulus [MPa] 800 470 Poisson’s ratio 0.3 0.3 Density [kgˇm–3] 7850 1229 1.2.2 Modification of Contact Parameters In current research, the calibration tests of coal particle parameters take the stacking angle at rest as the response variable. During the conveying process, the behaviour of bulk coal is not only macroscopic accumulation on the chutes, but also the mechanical interaction with the scraper conveyor. Therefore, it is necessary to modify the contact parameters of coal particles through the bulk coal transportation test with force and accumulation as the response variables. Using the actual scraper conveyor to carry out the transportation test is not feasible. Based on the transport principle, the rotary transport testing machine shown in Fig. 3a was designed. The upper sample has a wedge structure with the same slope as the scraper, as shown in Fig. 3e, and the material of the upper sample is 42CrMo (C: 0.38 % to 0.45 %), which is the same as the scraper. The lower samples are six fan-shaped plates with positioning holes, as shown in Fig. 3f, and the material of the lower samples is 16Mn (C: 0.13 % to 0.19 %), which is commonly used in the medium plate. The three-dimensional force sensor and the upper sample are fixed with the rack through the clamp, and the lower samples are fixed with the trough through the positioning holes. After the bulk coals with the required mass and size are paved in the trough, the motor drives the trough to rotate at a uniform angular velocity of 10 radˇs–1. The linear velocity of the position where the upper sample is located is the same as that of the scraper chain (1.1 mˇs–1), as shown in Fig. 3c. The bulk coals are pushed by the upper sample and accumulate around the sample, which is consistent with the principle of scraper transportation in actual work, as shown in Fig. 3c. The force on the upper sample when pushing the bulk coals was measured through the force sensor, as shown in Fig. 4. The stacking angle obtained using an image-processing program written in Matlab is 38.59°. Through Plackett-Burman and response surface tests, the quadratic polynomials between force, stacking angle and significant terms were established. The contact parameters obtained by solving the polynomials with the experimental force and stacking angle as the target values are shown in Table 2. Finally, the accuracy and applicability of the parameters were verified through tests under different transport conditions. Fig. 3. The test equipment and principle; a) test equipment; b) the rotary transport machine; c) test principle; d) three-dimensional force sensor; e) upper sample; f) lower sample; g) accumulation around the upper sample Response Analysis of a Scraper Conveyor under Chain Faults Based on MBD-DEM-FEM Fig. 4. The force on the upper sample Table 2. The corrected contact parameters Parameters Coal-Steel Coal-Coal Coefficient of restitution 0.65 0.64 Coefficient of static friction 0.401 0.333 Coefficient of rolling friction 0.032 0.041 1.3 DEM-MBD Coupling Simulation 1.3.1 Verification of DEM-MBD Coupling Model Due to the short laying length, there is a gap in the speed and tension with the actual situation. The point-by-point method is used to calculate the resistance of long-distance continuous conveying machinery; its main principle is shown in Eq. (1). This research focuses on the fault response of the scraper chain in the chutes, so it only verified the resistance of the loaded side (Points 3 and 4 in Fig. 1) and unloaded side (Points 1 and 2 in Fig. 1), which were calculated based on the parameters of the conveyor to be 348.9 kN and 35.3 kN respectively [23]. F i ˜F j °Wij , (1) where Fi [N] is the tension at the front point, Fj [N] is the tension at the back point, Wij [N] is the running resistance. The engagement and separation points of the sprockets and the chains are often selected as measuring points, as shown in Fig. 1. The DEM-MBD simulation was carried out under normal conditions to extract the chain tension at four points. According to Eq. (1), the simulation values of the running resistance on the load side and the unloaded side were calculated, and the comparison with the theoretical resistance is listed in Table 3, the relative error under no-load and full-load is less than 8 %, which testifies that the simplified model can reflect the mechanical characteristics of scraper conveyor. Table 3. The comparison between theoretical resistance and tension difference No-load Full-load Items i = 4 i = 2 i = 4 i = 2 j = 3 j = 1 j = 3 j = 1 Wij [kN] 35.2 35.2 348.8 35.2 .Fij [kN] 33.7 34.6 321.7 35.3 Relative error [%] 4.26 1.70 7.77 0.28 The chain tension at the middle section of the scraper conveyor provided by the cooperative enterprise (Yaxing Anchor Chain) is about 210 kN to 223 kN; the simulation tension is about 200 kN, and the maximum relative error between them is 10.3 %. The velocity curve of the chain under full-load condition is shown in Fig. 5, which reveals the fluctuation of “decrease–increase” after the velocity is accelerated. It conforms to the polygon effect [24] and validates the kinematics characteristics of simplified model. Fig. 5. The velocity of scraper chain 1.3.2 Design of Fault Conditions Chain breaking and chain stuck are the most frequent faults [25]. The small preload, rib spalling of coal wall, and coal blocks stuck at the connection of chutes can cause the chains to jam: we added irregular obstacles in the chute to simulate the chain stuck fault in dynamics software. The increased chain pitch caused by wear, the extreme impact load, the long­term over-carrying and the fatigue crack generated by the synergy of corrosion and other factors can cause the chain to break; we did not deal with the stuck fault in time and set the simulation script for the contact between chain links to simulate chain breaking fault in dynamics software. Wang, Z. – Li, B. – Liang, C. – Wang, X. – Li, J. 1.4 Experimental Verification of FEA Model 1.4.1 FEA Model of Chain The FEA model shown in Fig. 6 was employed to solve the non-linear contact between chain links. The size of the chain link made of 23MnNiCrMo54 (C: 0.22 %) is F 34 mm × 126 mm; the properties of the material are listed in Table 4, and the S-N curve is NS 2.49 = 1.30066×1010. The entire model was divided into tetrahedral mesh sized 6 mm, yielding 50,997 elements with 76,070 nodes. The frictional contacts were set between the flat chains and the vertical chain, and the coefficient of friction is 0.2. The right surfaces of flat chain 2 were subjected to boundary loads based on the coupling results, while the left surfaces of flat chain 1 were fixed. The pressure of coal was distributed on vertical chain 1 according to the pressure file derived from EDEM software. Fig. 6. The FEA model of the chain Table 4. The mechanical properties of 23MnNiCrMo54 Properties Value Density [kgˇm–3] 7860 Young’s modulus [GPa] 210 Poisson’s ratio 0.3 Tangent modulus [MPa] 2444 Yield strength [MPa] 1166 Tensile yield strength [MPa] 1254 1.4.2 Experimental verification The tensile testing machine (LAW-5000) was used to obtain the elongation rate of the chain under the test load (1160 kN to 1276 kN), as shown in Fig. 7, and the experiment was repeated three times. Based on the model in Fig. 6, the simulation value of the elongation rate is 1.33 %. The comparison between the experimental value and the simulated value is shown in Table 5. It can be seen from Table 5 that the relative error does not exceed 5 %. Fig. 7. The microcomputer controlled electro-hydraulic servo tensile testing machine (LAW-5000) Table 5. The comparison between experimental and simulation values of elongation Number Experimental value [%] Simulation value [%] Relative error [%] Average relative error [%] 1 1.40 5.00 2 1.36 1.33 2.20 4.07 3 1.40 5.00 2 SIMULATION RESULTS When the scraper chains are stuck or broken, the scraper de flects due to the uneven force on both ends, as illustrated in Fig. 8. The surrounding of the deflected scraper is divided into four areas, in which PQ is the stuck side and RS is the deflected side. Fig. 8. The areas around the deflected scraper 2.1 Dynamic Response 2.1.1 Operating Tension Fig. 9 is the tension diagrams of the measured chain links under faults. When the chain in areas PQ is stuck, the maximum tensions in areas Q and R reach 1139.9 kN and 669.9 kN, respectively, and the stuck side is Response Analysis of a Scraper Conveyor under Chain Faults Based on MBD-DEM-FEM Fig. 9. The tension curves; a) unilateral jamming; b) bilateral jamming; c) unilateral breaking; d) bilateral breaking larger than the deflected side; the tensions in areas P and S are reduced, and the minimums are both 0 kN. Furthermore, the tension on the stuck side changes earlier than the deflection side, but both sides return to stability concurrently. The dynamic responses of two chains are identical when both sides of the scraper are stuck, so only the curves on the side PQ are plotted. It can be seen from Fig. 9b that the maximum is 1155.9 kN, and the growth rate of tension is faster. The reason is that when both sides of the scraper are stuck, the scraper cannot deflect, and the growth rate of chain tension cannot be alleviated by deflection. According to the tension response under chain stuck fault, the areas Q and R were set as the fracture points. When the area Q fails, the tension in area P decreases and stabilizes at 10 kN to 15 kN due to medium doubled chain; the tension in areas R and S increases due to the augment of load, and the maximum is 947.8 kN. When the chains in areas Q and R fracture, the middle scraper deflects instantly, the front and rear scrapers deflect in the opposite direction, and the chains in areas Pand S are loose, as shown in Fig. 10. With the return of the front and rear scrapers and the deflection of the middle scraper, the chains finally break. 2.1.2 Velocity of Chains Fig. 11 reveals the velocity curves of the measured chain links under faults. In Fig. 11a, compared with the deflected side, the velocity fluctuation of the stuck side is larger, and the two fluctuations in area P are earlier and later than those in area Q severally. In addition, the chain links on the stuck side have the opposite velocity. The reason is that the chains in areas Wang, Z. – Li, B. – Liang, C. – Wang, X. – Li, J. P and Q are piled up and tensioned, respectively, and this state is transmitted forward and backward with the stuck scraper as the centre. As a result, the tension at the rear of the tested link along the transportation direction is greater than that at the front in a short time. The velocity at the front and rear of the scraper are synchronous when two sides are stuck. The lowest velocity is almost 0 mˇs–1, and there is a large positive mutation before recovery. When the chain in area Q breaks, the velocity in area P is reduced by the deflection and impact of the scraper, and the velocities in areas R and S increase with different amplitudes. When the chains at areas Q and R both break, the velocity in area P fluctuates from decrease to increase, and the velocity in area S increases. 2.1.3 Acceleration in the Z-Direction Fig. 12 shows the Z-direction acceleration curves of the measured chain links under faults. The scraper vibration can affect the chain vibration under normal conditions, and their vibrations are transmitted through the chain tension. From the chain tension curves under fault conditions (Fig. 9), it can be seen that when the PQ side of the scraper is stuck, the stuck scraper vibrates and deflects due to the external load. The scraper deflection loosens the chains in areas P and S, blocks the transmission of chain tension, causes the chain vibration in this area to weaken, and the minimum can reach 0. Additionally, the scraper deflection causes the chains in areas Q and R to be tightened, and the chain tension and its transmittance increases, resulting in the increase of chain vibration, up to 33 mˇs–2. Since the chain vibrations in areas R and S are the same as those in areas Q and P, respectively, they are not repeated in the figure. The results of bilateral jamming and unilateral fracture of the chain are the same as above. When the chains in areas Q and R break, the middle scraper deflects quickly, and then the front and rear scrapers deflect in opposite directions, as shown in Fig. 10. The remaining chains are piled up, the Response Analysis of a Scraper Conveyor under Chain Faults Based on MBD-DEM-FEM tension and its transmittance are reduced, resulting in reduced vibration of the chain and scraper. 2.2 Discrete Characteristics of Bulk Coal 2.2.1 Velocity of Bulk Coal The velocity of bulk coal under unilateral jamming is shown in Fig. 13. The farther the bulk coal is away from the scraper and chains, the lower the velocity. When the scraper starts to deflect, the velocity of the bulk coal in area Q decreases first; the velocity of the bulk coal in area S increases briefly. During the continuous deflection of the scraper to the maximum, the bulk coal in areas P and Q continue to accumulate, and the bulk coal in area S gradually return to the normal velocity. As the stuck side lags behind the deflected side by an angle, the velocity of the bulk coal in area Q instantly increases when the scraper returns to be steady, which can reach a maximum of 10.15 mˇs-1. The flying bulk coal may injure the staff. The result when both sides are stuck is the same as the result on the stuck side in Fig. 13. Fig. 14. The velocity cloud diagram of bulk coal under unilateral breaking; a) before fault; and b) after fault 2.2.2 Wear of Medium Plate A layer of coal with a smaller size is often covered between the medium plate and scraper chain. When the scraper chain moves, three-body abrasive wear is formed [13], as shown in Fig. 15. Due to the short simulation time, only the qualitative analysis and prediction of the wear area of the medium plate was carried out; the material of the medium plate is NM450 (C: = 0.35 %). The wear depth in the EDEM software was calculated by Eq. (2). h QA WFdA , (2) = = nt where Q [m3] is the volume of material removed, W [Pa–1] is the wear constant, the size is 1.2×10–12 Pa–1, Fn [N] is the normal force, dt [m] is the tangential distance moved, and A [m2] is the area of material removed. Wang, Z. – Li, B. – Liang, C. – Wang, X. – Li, J. a) furrow effect; b) micro-cutting effect It can be seen from Fig. 16 that the wear area of the medium plate is concentrated near the chain paths, which is consistent with the wear area of the scrapped medium plate. The unilateral jamming condition deepens the wear at the chain paths. In addition to deepening the wear at the chain paths, the bilateral jamming condition also form new wear areas at the centre and both ends of scraper. When the chain fractures, curved wear marks are generated on the medium plate due to the severe deflection of scraper. a) normal; b) unilateral jamming; c) bilateral jamming; d) chain breaking; e) the actual wear area 2.3 Results of FEA 2.3.1 Static Structural Analysis The results of static analysis under normal load and stuck fault load were obtained by applying tension of 180 kN and 1155.9 kN to the FEA model, shown in Figs. 17 and 18. The maximum equivalent stress occurs at the inner side of the connection between the curved section and the straight section. The equivalent stress and elastic strain under normal load are less than the yield strength. The maximum equivalent stress under fault load is 1238.2 MPa, which is higher than the yield strength, and the maximum plastic strain is 0.10098. The above results explain that the chain has a high degree of damage when the chain is stuck. To prevent chain breaking accident, the damaged chain should be replaced in time. Fig. 17. The results of statics analysis under normal load; a) equivalent stress, b) equivalent elastic strain a) equivalent stress, b) equivalent elastic strain Response Analysis of a Scraper Conveyor under Chain Faults Based on MBD-DEM-FEM Based on the equipment in Fig. 7, the deformation results of the chain were verified. The comparison of elongation is shown in Table 6. It can be seen that the relative error between simulationand experiment does not exceed 4 %. Table 6. The comparison between experimental and simulation values of elongation Experimental value Simulation Average relative Number [%] value [%] error [%] Normal load 0.220 0.225 0.223 0.215 3.43 Fault load 1.27 1.25 1.28 1.22 3.68 2.3.2 Fatigue Life According to the coupling results, the load history for one cycle under normal conditions was constructed, shown in Fig. 19. The fatigue lifewas calculated based on the results of static analysis and linear cumulative damage theory. The theory can be expressed as: m D=(n/N),(3) . ii 1 where D is the total damage, ni is the number of cycles under stress level, Ni is the life under stress level, m is the number of stresses. Based on Fig. 19, the stuck fault load was added to obtain the fatigue life. The fatigue life is displayed in Fig. 20. The lowest parts of the life under the two conditions are the contact position and the inner side of the connection, and the lowest values are 20,070 and 383.8 cycles respectively. The life under normal load is 52 times that under fault load. The chain near the stuck position should be overhauled and replaced in time to prevent the chain from breaking. 3 ANALYSIS OF FACTORS AFFECTING CRACK GROWTH 3.1 Plackett-Burman Test 3.1.1 Design of Test Defects in structure or material are inevitable, of which cracking is the most common. According to load and propagation form of the cracks, they can be divided into three types: open type, slip type, and tear type, as shown in Fig. 21. Stress intensity factor (SIF) K is defined as the strength of the stress field around the crack front of linear elastic material. The main stresses in the chain link are tensile and compressive stresses. The fracture type of chain link is Type-I, which SIF K1is expressed as: K= afaW), 1˜°(,(4) Wang, Z. – Li, B. – Liang, C. – Wang, X. – Li, J. where s [MPa] is the normal stress, a [mm] is crack size, W [mm] is the size of the component in the direction of crack depth, f (a,W) is the shape correction function. The Plackett-Burman test determines the significance of the factor by comparing the response variables at different levels of each factor [26]. Based on the results of FEA, a semi-elliptical crack was set on the inner side of the connection. Through pre-simulation, it is found that the positive or negative initial angle of the crack in Fig. 22 does not affect the result. Therefore, the crack depth, depth-length ratio, initial angle and tensile load were taken as factors, and the maximum K1max of the type-I SIF at the crack front was used as the response variable to carry out the screening test. Each factor was set to high and low levels, as listed in Table 7, where the virtual parameters are used for error estimation. Parameters Low (–1) High (+1) Crack depth L [mm] 0.2 1 Depth to length ratio B 0.2 0.8 Initial angle A [°] 0 60 Tensile load F [kN] 90 270 Virtual parameters -1 +1 3.1.2 Results of Testing Table 8 demonstrates the plan and results of test, and the significance ranking of factors on K1max is listed in Fig. 23. From the pareto chart, the tensile load, initial angle, and crack depth are significant on K1max (t > 2.36462), and the depth to length ratio is not significant. The tensile load and crack depth are positive effects, and the initial angle is negative. The order of significance is F > A > L. Therefore, the significant factors were selected for the following tests. Table 8. The plan and result of the Plackett-Burman test Number L B A F K1max [MPaˇmm–1/2] 1 1 1 –1 1 682.48 2 –1 1 1 –1 31.16 3 1 –1 1 1 299.59 4 –1 1 –1 1 309.75 5 –1 –1 1 –1 43.38 6 –1 –1 –1 1 424.65 7 1 –1 –1 –1 293.58 8 1 1 –1 –1 226.25 9 1 1 1 –1 71.88 10 –1 1 1 1 94.07 11 1 –1 1 1 299.59 12 –1 –1 –1 –1 140.78 3.2 Single Factor Test The design of the single factor test is shown in Table 9, and the depth to length ratio is fixed at 0.5. The K1 on the crack front is plotted as Fig. 24, where the ratio of the arc length from a point to the end of crack front to the total arc length is the relative position of the point. In Fig. 24, K1 aggrandizes with the increase of crack depth, and the growth rate gradually slows down. K1 is negatively correlated with the initial angle; the larger the initial angle,the greater the decrease in K1, and the smoother the curve of K1. K1 is positively correlated with the tensile load, and the increase of K1 on the crack front is almost constant; the lower the load, the smoother the curve. In addition, as the initial angle increases, the maximum position of K1 moves to the end of crack front near the load side. Response Analysis of a Scraper Conveyor under Chain Faults Based on MBD-DEM-FEM Table 9. The design of the single factor test Number L [mm] A [°] F [kN] 1 0.2 to 1.0 0 180 2 0.6 0 to 60 180 3 0.6 0 90 to 270 a) crack depth; b) initial angle; c) tensile load 3.3 Box-Behnken Test 3.3.1 Design of Test Based on the results of the Plackett-Burman test, the Box-Behnken test was designed with the K1max as the response value. Each factor was set to high (+1), medium (0), and low (-1) three levels, as listed in Table 10. The entire design includes 12 sets of factorial tests and three sets of central tests for error estimation. Table 10. The factors and levels of Box-Behnken test Factors –1 0 +1 L [mm] 0.2 0.6 1 A [°] 0 30 60 F [kN] 90 180 270 3.3.2 Results of Test Based on the results of the test in Table 11, the quadratic regression polynomial between factors and K1max was constructed by Design-Expert software as: 1max....°382LA K˜°1246.24542L.549A.15F...LF°0026AF°12634L2°2 146..3005.A°000017.F2.(5) Table 11. The results of the Box-Behnken test Number L A F K1max [MPaˇmm–1/2] 1 -1 -1 0 239.98 2 1 -1 0 509.29 3 -1 1 0 72.71 4 1 1 0 158.86 5 -1 0 -1 92.02 6 1 0 -1 196.12 7 -1 0 1 277.36 8 1 0 1 591.39 9 0 -1 -1 201.01 10 0 1 -1 62.137 11 0 -1 1 605.63 12 0 1 1 187.59 13 0 0 0 310.23 14 0 0 0 305.57 15 0 0 0 316.51 The variance analysis of the regression polynomial is listed in Table 12. The model is extremely prominent (P < 0.0001), and the lack of fit is not prominent (P = 0.0839 > 0.05). The coefficient of variation (CV) is small (5.3 %), the R Square before (0.998) and after (0.993) correction is close to 1, and the accuracy is high (46 > 4). It can be concluded that the constructed quadratic model has high fitting accuracy and can characterize the relationship between K1max and factors. The significance of term Wang, Z. – Li, B. – Liang, C. – Wang, X. – Li, J. was acquired by F-test. The larger the F value, the more prominent the impact. When the significance level is 0.05, the significances of terms are ranked as: F> A> L> AF> LF> LA> A2> L2. Table 12. The variance analysis of polynomial Source Sum of Squares F-value P-value Model 4.21E+05 220.05 < 0.0001 L 74805.19 351.97 < 0.0001 A 1.44E+05 679.18 < 0.0001 F 1.54E+05 725.54 < 0.0001 LA 8386.9 39.46 0.0015 LF 11017.65 51.84 0.0008 AF 19483.55 91.67 0.0002 L2 1508.79 7.1 0.0446 A2 7592.13 35.72 0.0019 F2 6.56 0.0309 0.8674 Lack of Fit 1002.4 11.09 0.0839 R2 = 0.998; Ra 2 = 0.993; CV = 5.3 %; Precision = 46 3.3.3 The Effect of Interaction Through the three-dimensional response surface graph, the relationship between the interaction and the response value can be understood intuitively. When a factor is at 0 level, the variation of K1max with the interaction is shown in Fig. 25. It can be seen that the crack depth and tensile load are positively related to the rate of change of K1max with other factors, while the initial angle has a negative effect on the rate of change of K1max with other factors. 4 CONCLUSIONS In this paper, a fault simulation method of scraper conveyor based on MBD-DEM-FEM coupling was used. The DEM-MBD-FEM models were established and verified by experiments and theory, the response data of the chain and bulk coal under chain stuck and chain-breaking faults were obtained, and some results were verified. For the fracture failure, the single factor test and response surface test were used to investigate the single and interactive effects of factors on the SIF. 1. With the force and stacking angle as response values, a rotary transport testing machine was designed to modify the contact parameters of coal. The DEM-MBD coupling model was verified, and the relative error between tension difference and resistance is less than 8 %. The static structure model of the chain was verified Fig. 25. Response surface of interaction to K1max; a) DA; b) DF; c) AF by the chain tensile experiment, and the relative error is less than 5 %. 2. In chain jamming and breaking faults, the deflection of scraper causes the uneven stress on chains. The tension of the tensioned chain increases rapidly, resulting in the torsion and vibration of chains. The tension, velocity, and Response Analysis of a Scraper Conveyor under Chain Faults Based on MBD-DEM-FEM vibration of the loose chain decrease. The fault load leads to the plastic deformation of chain link and reduces its fatigue life. The wear of the medium plate is concentrated on the chain paths, which is consistent with the actual situation. In addition, the chain faults cause new wear on the centre and both sides of the medium plate surface. 3. The tensile load, initial angle and crack depth are significant on K1. 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International Conference on Computer Technology, Electronics and Communication, p. 866-870, DOI:10.1109/ICCTEC.2017.00192. [25] Bai, J., Xie, M.J. (2016). Scraper conveyor fault statistics and analysis of Shendong mining. Coal Mine Machinery, vol. 37, no. 11, p. 141-143, DOI:10.13436/j.mkjx.201611050. (in Chinese) [26] Xia, R., Li, B., Wang, X.W., Yang, Z.J., Liu, L.P. (2019). Screening the main factors affecting the wear of the scraper conveyor chute using the Plackett-Burman method. Mathematical Problems in Engineering, vol. 2019, art. ID 1204091, DOI:10.1155/2019/1204091. Response Analysis of a Scraper Conveyor under Chain Faults Based on MBD-DEM-FEM Investigation on the Depth of Slip Hanger Teeth Bite into Casing and the Mechanical Properties of Casing under Different Suspension Loads in Ultra-Deep Wells Chen, Y. – Tan, J.J. – Xiao, G.P. Yong Chen – Jinjin Tan – Guoping Xiao Southwest Petroleum University, School of Mechanical and Electrical Engineering, China There is great difficulty in controlling the setting load of the large-size slip casing hanger in the Northwest Oilfield in China, and a reasonable setting load is of great significance. This paper studied the relationship between slip hanger bite depth and suspension load in the . 273 mm WE-type slip hanger in the Northwest Oilfield in China through experiment, theoretical computation, and finite element analysis. The accuracy of the finite element model was proved by comparing the finite element simulation results with the experimental bite marks on the casing surface. The study results show that the bite mark of the slip insert in the casing is deeper in the lower part of the sitting position. When the hanging load increases from 1000 kN to 6000 kN, the maximum bite depth of the slips in the casing gradually increases with the suspension load. The residual collapse strength of the casing decreases correspondingly. When the residual collapse strength decreases to a certain value, the maximum suspension force corresponding to the bite depth of the slip insert can be obtained. Based on the finite element research results and theoretical equations, the stress distribution on the casing wall where the slips bite the deepest is obtained by derivation. The suggestions on improving the material structure of the casing under this stress were proposed. The limit of the setting load of the large-size casing wellhead for avoiding casing collapse was obtained, which is of great significance for guiding field-casing setting. Keywords: WE-type slip hanger, suspension load, slip hanger bite depth, finite element simulation, casing Highlights • The relationship between slip hanger bite depth and suspension load has been studied. • The bite depth of the slips is gradually reduced from no. 1 to no. 17 (from bottom to top) under a specific suspension load. • The maximum circumferential squeezing stress on the inner wall of the casing is the risky section, which is subjected to the deepest bite by slip inserts. • As the suspension load increases, the casing residual collapse strength decreases. • Comparing the experimental result with the finite element result, shows that the two are consistent. 0 INTRODUCTION There is increasing difficulty in controlling the setting load due to increasing large-size casing tripped into deep reservoirs in the Northwest Oilfield in China. Excessive setting loads and the high-pressure and high-temperature (HPHT) conditions of reservoirs (such as 70 MPa and 170 °C)will cause problems such as wellhead subsidence, and severe damage to the casing caused by slips and the sitting load is too small possibly lead to poor sealing of the casing head [1] and [2]. Tripping the liner hanger into the upper casing is completed through interaction between the liner hanger slips and the casing. The setting of the liner hanger causes variation in the initial stress distribution inside the casing. The additionalcircumferential stress and additional axial stress caused by slips increase the load on the casing and affect the safety performance of the casing; those beyond the original design loads significantly lessen the margin against piping failure [3]. Statistics found that there were 10 well-times of large-size deep well casings in the past two years, and 3 well-times had abnormalities during wellhead installation. Therefore, a choice of setting load to solve the above contradictions and recommended practices for the setting of large casings are of great significance. Many efforts have been put in analysis of the force and structure of the slip hanger and casing. Li et al. [4] and Tong et al. [5] carried out the mechanical analysis of slips on the packer with statics principles and obtained the formula for calculating the normal stress on the slip insert during anchoring of the slips and. Tang et al. [6] aim at the problem of damage caused by the casing pressure on the slip after setting, the pressure-bearing law of the slip teeth under the three-tooth parameters was analysed. Mohammed et al. [7] focused on factors attributing to casing failure, their failure mechanism and the resulting failure mode. The slip-casing bite model was analysed using the finite element numerical simulation method, which has been widely used in recent years. Wang et al. [8] carried out the finite element analysis of the force process of slips and improved the structural parameters of slips. However, when the well’s casing head pressure cannot be permanently bled off with a needle valve, the casing is said to exhibit sustained casing pressure (SCP) [9]. A cement sheath can act as a secondary barrier element in a dual barrier (elastomer seal and cement sheath) zonal isolation system. Cement is the main physical barrier able to seal fluid flow into unintended zones from the wellbore [10] and [11]; the rubber materials can be squeezed into the annulus between the hanger and the upper casing, effectively sealing the annulus, and providing an efficient two-way pressure-bearing capacity [12] to [14]. In this study, the relationship between slip insert bite depth and suspension load of the WE-type slip hanger of the Northwest Oilfieldin China was studied using finite element analysis methods and theoretical computation. The limit of the setting load of the large casing wellhead for avoiding casing collapse was obtained. The formula for calculating the internal stress distribution of the risky section of the casing was derived to guide the design of the well structure and field operation. 1 THEORETICAL ANALYTIC METHODS Current well-drilling and operation technologies cannot provide cost-effective solutions for emerging challenges. Based on statistics, casing deformation was observed in 38 wells out of 72 horizontal wells after hydraulic fracturing [15] and [16]. According to the field survey, there is always a section with the largest bite depth when the slip hanger holds the casing, i.e., the risky section. Lowering the pH value will cause an electrochemical reaction. It is common for the casing material to be exposed to the H2S/CO2 acid gas environment, and electrochemical reactions are prone to occur in risky sections [17] to [19]. In order to roughly clarify how the casing will be destroyed after exceeding the limit suspension load, the distribution of circumferential normal stress, radial normal stress, circumferential and radial shear stress of the dangerous section of the casing is determined by the theory of elastoplastic mechanics [20]. Fig. 1. Diagram of stress on risky section of casing As shown in Fig. 1, the risky section of the casing is simplified as a circular ring. The outer wall of the ring is bitten by the slip inserts to produces a squeezing force. If the casing body force is neglected, the force is axisymmetric, and the stress component is written as [20]: A ˜..B12ln°2C,(1) ..ˆ. °°2 ˜...A2ˆB.32ˆln°.ˆ2,(2) C ° ˜.˜.0.(3) °..° Boundary conditions: .˜°..ˆ0,(4) °ˆa .˜°..ˆ0,(5) °ˆb ()..,(6) ˜°°a 0 ˜(7) ()b... °°.qb According to Eq. (3), Eqs. (4) and (5) are satisfied. Eqs. (6) and (7) are expressed as: A2˜B°12˜lna.˜2C.0,(8) a A˜B°˜lnb2..qb.(9) 12.˜C b2 A compatible equation is expressed as: 22 .˜1˜1˜ˆ2 °°.0,(10) .222 ˜˜˜ .. which satisfies the compatibility equation. The balanced differential equation (casing body­weight neglected) is expressed as: .°..2. 1.˜.˜. ˆˆ.0,(11) ˜...˜˜ ˜.˜.... ˆ1ˆ.ˆ. °°.0,(12) ˜ˆˆ˜ˆˆ which satisfies the balanced differential equation. According to the displacement single value condition, B =0, then: 222 abq°qb A˜b,2C˜b,(13) 2222 b°ab°a which are incorporated into Eqs. (1) and (2) and re­arranged to obtain the Lame solution of casing stress: Investigation on the Depth of Slip Hanger Teeth Bite into Casing and the Mechanical Properties of Casing under Different Suspension Loads in Ultra-Deep Wells 517 a2 1. ° ˜°..a22qb,(14) 1. b2 a2 1ˆ ° ˜...a22qb,(15) 1. b2 where . is casing radial length interval, .. [a, b], s. casing radial normal stress, sf circumferential normal stress on casing, t.f, tf. circumferential and radial shear stress, A, B, C arbitrary constants, a, b casing inner and outer diameters, qb external stress of slips on the casing, and F stress function. Fig. 2. Distribution of stresses in the polar coordinates The above quantities are described in the form of polar coordinates (Fig. 2); the specific direction and size are determined according to the calculated results. Fig. 3. Distribution of stress inside the casing radial risky section According to derivation, a, b and qb in expression s. are all constants, and s. is only positively correlated to a2/.2. Similarly, sf is only positively correlated to –a2/.2. According to theoretical derivation, the distribution of the radial normal stress and circumferential normal stress on the casing is obtained (Fig. 3). 2 EXPERIMENTAL APPROACH AND PROCEDURE 2.1 Experimental Apparatus The experiment was carried out on a horizontal hydraulic experiment box, model XDL-10000 kN, and its main technical indicators are shown in Table 1. Table 1. Technical indexes of XDL-10000 kN horizontal hydraulic experimental chamber Item Parameter Maximum test force 10000 kN Hydraulic cylinder piston stroke 1000 mm Stretching chuck distance up and down 6 m Test machine space 500 mm × 6000 mm Machine host size 9000 mm × 1200 (L × W × H) mm× 800 mm Hydraulic system power 5 kW The total weight of the machine 9000 kg Fig. 4. Hydraulic experimental chamber Fig. 4 shows the test machine used for casing tensile testing. The body adopts a double-frame force frame structure; a single-hydraulic cylinder is installed on the upper beam, the piston and sensor through the force frame and the upper tensile fixtures are directly connected on the specimen tensile test, Chen, Y. – Tan, J.J. – Xiao, G.P. so that the loading head structure is simplified; The reaction frame and the left and right rigid frame are connected to the main body of the testing machine; The down-stretching chuck body is connected with the cross beam on the other side of the frame. The side of the host machine is equipped with a hydraulic loading system, which adopts a low-noise hydraulic pump unit, a hydraulic valve group, and a high-precision electro-hydraulic control valve to ensure high precision, high efficiency, low noise and fast response of the system, and to realize the automatic control of loading and reversing of the test (Fig. 5). Fig. 5. The experiment schematic diagram 2.2 Experimental Procedure In the experiment, the load increased by 500 kN uniformly over time, and the axial stress on the slips simultaneously measured was plotted as shown in Fig. 6. As show in Fig. 6 that as the load increases, the maximum normal stress on the casing also increases, and the function fitted according to the curve is a third-order equation. It can be seen that the influence of the load on the results of the research is nonlinear. 3 FINITE ELEMENT ANALYSIS AND MATERIAL PREPARATION 3.1 Structure of . 273 mm WE-Type Slip Hanger In this paper, the . 273 mm WE slip hanger commonly applied in the Northwest Oilfield in China was studied. The 3D solid structure established according to the real size is illustrated in Fig. 7. a) b) Fig. 7. a) WE-type-slip hanger assembly diagram; b) WE-type slip hanger exploded view According to Fig. 7, the slip hanger consists of 1 hexagon socket screw; 2 pressure plate; 3 sealing ring; 4 slip shell; 5 slip teeth; 6 screw; 7 positioning block 3.2 Working Principle and Analysis of Stress on WE-Type Slip Hanger The stress on the casing head slips is illustrated in Fig. 8. Investigation on the Depth of Slip Hanger Teeth Bite into Casing and the Mechanical Properties of Casing under Different Suspension Loads in Ultra-Deep Wells 519 The process of slip bite in the casing is one of interaction between the slips and the pipe string. When Slip 2 starts to bite into Casing 1, the active force comes from the initial friction force received by the slips. The slips will hold the pipe string radially by causing reactions to the slips through amplifying them with the casing weight. In this process, the casing causes a large vertical gravitational acceleration under the action of the huge suspension load, and this causes the mutual friction between the casing and the slip insert tip, which propels the slip to move downward along Shell 3 until the slips cannot slide. Then, the slips are in direct contact with the slip shell through the inclined plane, and the casing moves downward to squeeze the slips, which remain in the same position by the support of the slip shell. The slip teeth are squeezed into the casing to generate a squeezing force; thus, sufficient friction is created to reach a mechanical equilibrium on the casing. If the suspension weight of the downhole casing increases, the slips continue to bite into the casing due to greater squeezing force until a new balance is reached, and the safe suspension of the casing is realized. According to Fig. 8, the slips are subjected to the squeezing force and the frictional force of the casing and the slip shell; enough friction is generated by the bite of the slip teeth into the casing to balance the casing gravity. Therefore, for slips, we obtain according to the principle of static balance: FF32sin.°F(16) ˜cos., 1f2 Fsin˜°Fcos˜.G,(17) 2f32 where F1 is the supporting force of the casing on the slips [N], F2 the supporting force of the slip shell on the slips [N], Ff 32 the frictional force of slip shell on the slips [N], Ff 12 the friction force of the casing against the slips [N], Ff 23the friction force of the slips against the slip shell [N], a the slip cone angle [ş], and G the casing weight [N]. 3.3 Establishment of Finite Element Model of WE-Type Slip Hanger The parameters of material properties input in the process of finite element modelling are listed in Table 2. The model’s geometric dimensions, loads, and boundary conditions are symmetrical with regard to the axis. An axisymmetric finite element model is established to reduce the computational effort. Full constraints are imposed on the slip shell, and the suspension weight is simplified as an axial load acting on the casing. Finite element analysis (FEA) is an established standard procedure in many areas of numerical strength analysis[21] and[22]. According to the structure of the WE-type casing hanger and casing, assumptions are also made as follows to simplify the analysis model: (1) The material is an elastoplastic body with isotropic and homogeneous properties; (2) The effect of ovality and wall thickness unevenness of casing hanger and casing is neglected; (3) The effect of the error in wellhead suspension installation is neglected. A finite element model of the slips holding the casing was established according to the real hanger structure, and the model was meshed, as shown in Figs. 9 and 10. Mesh refinementof the slip inserts, the Table 2. Parameters of material properties of metal components Component Slips Slip bowl Casing head Material 718 30CrMo 30CrMo Density [kg/m3] 7850 7850 7850 Young’s modulus [MPa] 249350 249350 249350 Poisson’s ratio 0.3 0.3 0.3 Yield strength [MPa] 980 785 517 Tensile strength [MPa] 1550 930 655 520 Chen, Y. – Tan, J.J. – Xiao, G.P. casing outer wall, the clip side surface, and the inner surface of the slip shell was carried out to obtain the accurate calculation results of contact between slips and the casing outer wall and between the slip side surface and the slip shell conical surface. 4 RESULTS AND DISCUSSION 4.1 Comparison of Results The finite element simulation results are compared with the experimental bite marks on the casing surface to verify the correctness of the analysis results, as shown in Fig. 11. According to Fig. 11, when the slip is holding the casing, the slip teeth squeeze the casing and bite into the casing wall. The slip teeth leave a bite mark with a clear texture on the casing surface. When the suspension load reaches a certain value, plastic deformation occurs on the bite mark. In addition, the simulated bite marks show that the bite marks are deeper in the lower part of the setting position. It can be proved that the establishment and analysis results of the finite element model are consistent with the experiment results. a) b) Fig. 11. a) Experiment bite marks on the casing surface; b) simulated bite marks on the casing surface 4.2 Relationship between Bite Depth and Suspension Load The maximum depth of the slips bite into the casing when the suspended load is increased from 1000 kN to 6000 kN is simulated, as shown in Fig. 12. The maximum height of the slips bite into the casing gradually increases with the suspension load, which gradually enhances the plastic deformation of the casing surface. When the slips bite into the casing surface, the bite depth of each tooth into the casing is different. Therefore, the teeth on the same slip are numbered sequentially from bottom to top, and the bite depth of all 17 teeth of the . 273 mm WE-type hanger slips into the casing under different suspension loads was obtained, as illustrated in Fig. 13. Investigation on the Depth of Slip Hanger Teeth Bite into Casing and the Mechanical Properties of Casing under Different Suspension Loads in Ultra-Deep Wells 521 hmax = 0.0716mm hmax =0.0130mm hmax =0.1919mm tt t F =1000 kN F =2000 kN F =3000 kN hmax =0.2566mm hmax =0.3220mm hmax =0.3947mm F =4000 kN tt F =5000 kN F =6000 kN t Fig. 13. Number of slip hanger’s teeth same suspension load, equivalent stress on the casing and the bite depth of slips gradually decreases from no. 1 to no. 17 (from bottom to top). With the increase of the suspension load, the bite depth of the slips gradually increases, and the increase in the bite depth of the slips is higher in the lower positions (smaller number). According to the finite element simulation results, the relationship between the suspension force and the maximum bite depth of the slip can be obtained, as shown in Fig. 16, which is used to fit the formula of the relationship between the suspension load and the maximum bite depth of the slip: hmax˜7E°11.6E°8x..x00129,(18) x3200006.. where hmax is the maximum bite depth of slips [mm]; x is the suspension load [kN]. According to Figs. 14 and 15, the bite depth of Fig. 16. Relationship between the suspension load and the slip inserts into the casing is differentiated under the maximum bite depth of slips 4.3 Relationship between the Suspension Load and the Residual Collapse Strength When the slip inserts bite into the casing, the mechanical properties of the casing varies with the bite depth of the slips, which leads to variation in the collapse strength and suspension ability of the casing. Therefore, it is of great significance to study the effect of the bite depth of slips on the collapse strength and suspension ability of the casing. The cross-sectional schematic of the slip teeth bite into the casing is shown in Fig. 17. Assuming that the suspension load of the hanger at a certain moment is F, the corresponding maximum height of the slip insert bite into the casing is hmax. If the casing cross-sectional area is collapse and elastic-plastic collapse, and casing steel grade coefficient. The relationship between the suspension load and the residual collapse strength of the wellhead casing was obtained by integrating Eqs. (18) to (20), as shown in Fig. 18. As the suspension load increases, the casing residual collapse strength decreases. When decreases to a certain value, the maximum suspension force corresponding to the slips at a certain bite depth can be obtained. In practical application, considering the uneven wellhead or the error during installation of casing head, the residual collapse strength of the casing obtained in Fig. 18 should be divided by a “working condition factor”. AC [mm2], the residual wall thickness of the casing at the maximum bite position is ts = t – hmax [mm], the residual diameter DS = D –2hmax [mm], and the axial tensile stress is Sa = F /AC [MPa]. The equivalent yield strength of the casing under the action of axial tensile stress is written as:  .Sˆ2S aa Y˜1075...°05.Y,(19) °.ppa.YpYp ..  where Ypa is the equivalent yield strength of the casing under axial tensile stress [MPa], Yp is the yield strength of the casing [MPa]. When (D /t)YP =DS /tS =(D /t)PT, is combined with the API formulafor plastic collapse pressure and takes the safety factor as 1.5, the residual plastic collapse strength is written as: .Aˆ P˜Y°B/.°.C PSPa..156894757,(20) Dt/ .Ss. where PPS is residual plastic collapse strength [MPa], (D /t)YP is D /t limit value of yield strength collapse and plastic collapse, is the limit value of plastic Suspension load [kN] Fig. 18. Relationship between the suspension load and the residual collapse strength of casing at the wellhead 5 CONCLUSIONS An axisymmetric finite element model of the hanger suspended the casing was established based on the real structure of the .273 mm WE slip hanger. The accuracy of the finite element model was proved by comparing the simulation results with the real bite marks on the casing surface. The bite depths of slip inserts into the casing are different under a specific suspension load. The bite depth of the slips is gradually reduced from no. 1 to no. 17 (from bottom to top). With the increase of the suspension load from 1000 kN to 6000 kN, the maximum bite depth of slip inserts into the casing gradually increases from 0.07163 mm to 0.3947 mm, and the increase in the bite depth of the slips is higher in the lower positions (smaller number). As the suspension load increases, the residual collapse strength of the casing decreases. 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International Journal of Simulation Modelling, vol. 19, no. 1, p. 100-111, DOI:10.2507/IJSIMM19-1-506. Chen, Y. – Tan, J.J. – Xiao, G.P. Strojniški vestnik - Journal of Mechanical Engineering 67(2021)10, 525-533 Received for review: 2021-05-06 Š 2021 Journal of Mechanical Engineering. All rights reserved. Received revised form: 2021-07-27 DOI:10.5545/sv-jme.2021.7246 Original Scientific Paper Accepted for publication: 2021-09-24 Optimization of Laser Parameters and Dimple Geometry Using PCA-Coupled GRG Saravanan, K.G. – Thanigaivelan, R. Saravanan Kandasamy Ganesan1 – Thanigaivelan Rajasekaran2,* 1 Sona College of Technology, India 2 Muthayammal Engineering College, India Stainless steel (SS316L) is applied in numerous fields due to its intrinsic properties. In this study, micro-dimples were fabricated on SS316L. The effects of laser process parameters, such as frequency, average power, and pulse duration, on the average dimple diameter, dimple distance, and dimple depth were studied using an L9 orthogonal array. The analysis of variance (ANOVA) and multi-objective optimization technique, principal-component-analysis-coupled grey relational grade (GRG), was used to optimize laser process parameters on output responses. The optimal machining parameter settings obtained for the highest GRG peak value of 0.2642 are 15 kHz (frequency), 12 W (average power), and 1500 ns (pulse duration). The ANOVA results showed that average power is the most influential factor, contributing 86.40 % to performance measures (average dimple diameter (f), dimple distance (d), and depth (l). Moreover, the effect of process parameters was studied using mean effect plots, and the micro-dimple quality was analysed using SEM micrographs. Keywords: laser, micro-dimple, principal component analysis, grey relational grade, stainless steel Highlights • Micro-dimples were fabricated on SS316L. • The effects of laser process parameters, such as frequency(F), average power (P), and pulse duration (t) on the average dimple diameter (f), dimple distance (d), and depth (l) were studied using an L9 orthogonal array (OA). • The analysis of variance (ANOVA) and multi-objective optimization techniques, including grey relational grade (GRG) and principal component analysis (PCA), were used to optimize laser process parameters on output responses. • In the sequence of the significance of controllable factors to the multi-performance characteristics in the laser process is as follows: frequency, pulse duration, and average power. 0 INTRODUCTION Stainless steel finds applications in food processing, pharmaceutical equipment, medical devices, potable water, and wastewater treatment industries due to its improved corrosive resistance property. Although stainless steel alloys have excellent properties, including corrosion resistance, higher strength and hardness, their tribological action is not satisfactory in biomedical applications, in particular for hip prostheses that require sliding contact [1]. Moreover, for biomedical applications, stainless steel alloy surfaces must be modified for tissue attachment and wear resistance [2]. Hence, their surface properties must be improved, and numerous methods, such as plasma and flame coatings, shot peening, thermal oxidation, nano-composite coatings, and ball burnishing, are available [3]. Each process has advantages and limitations. In this study, laser texturing was employed to improve the wear resistance of the stainless steel alloy surfaces. Laser texturing can be used to modify the SS-316L surface by generating micro-dimples to improve its properties under service conditions. Kovaci and Seçer [4] investigated the effects of different surface textures acquired through selective laser melting on the mechanical properties of AISI 316Lstainless steel. Their results showed that the effects of surface textures and the area density on friction and wear properties were highly significant. Liew et al. [5] studied the effect of the dimple geometry with different textural shapes and orientations on friction and reported that geometric factors, such as the shape dimple area, area ratio, and dimple depth, considerably influence the load-carrying capacity of contacting surfaces. Yu et al. [6] investigated the effect of different dimple shapes on the tribological performance of surface textures. The dimple area ratio and depth considerably affect the friction reduction. According to the literature, surface modification plays a key role in improving the mechanical properties of materials. The fabrication of micro-dimples by using laser and control of process parameters on geometrical features were considered in [6]. Li et al. [7] fabricated the micro-dimples by using laser peen texturing on SUS304 stainless steel and found that the diameter, depth, and aspect ratio of the micro-dimples increased with laser power density. Dai et al. [8] used masked laser ablation to fabricate a micro-dimple array on a substrate surface with a pulsed Nd:YAG laser, investigated the influence of processing parameters on the micro­dimple morphology, and developed clean periphery micro-dimples. Ahmed et al. [9] reviewed the effect of the geometrical parameters of micro-dimples on piston/cylinder assembly and concluded that the high dimple depth is unfavourable for development for starved lubrication. The most crucial parameter to enhance the tribological performance of piston ring/ cylinder assembly is the aspect ratio [9]. Won and Kim [10] developed masked laser surface texturing to produce hundreds of micro-patterns. The number of laser pulses substantially affects uniform pattern formation [10]. Puoza et al. [11] micro-machined 45 steel, GCr15 steel, and grey cast iron materials by using laser processing technologies and analysed the features, including the diameter and depth, of the ablated micro-dimples. The diameter and depth of the micro-dimples generally increased with an increase in the laser fluence for all the materials [11]. Behera et al. [12] fabricated micro-channels and micro-dimples with different dimensions on anSS-304 alloy surface by using the pulsed Nd:YAG laser beam and studied the effects of process parameters, such as laser scanning speed, current, laser pulse frequency, and pulse duration, on the diameter and depth of micro-features. The dimensions of micro-features decreased an increase in the scanning speed and increased with an increase in the pulse frequency, pulse duration, and current [12]. Researchers have synthesized various micro-features with diverse dimensions for numerous applications. To improve the adherence of coatings with substrates, Lamraoui et al. [13] fabricated micro-dimples with a diameters and depths of 60 ľm to 90 ľm and 60 ľm to 120 ľm, respectively. Teleginski et al. [14] textured micro-dimples of 200 ľm diameter on a material. Gajrani et al. [15] developed micro-dimples with the diameter and depth of 250 ľm and 50 ľm on a cutting tool for lubrication retention. Researchers have used lasers to modify alloy surfaces for various applications, and the optimal use of laser process parameters for efficient production and high accuracy requires the proper control of input parameters. Although lasers have been used for alloy surface modification, no systematic study has reported the optimization of the laser process by using a multi-objective optimization technique. Hence, in this study, micro-dimples were fabricated on SS316L, and the effects of laser factors such as frequency (F), average power (P), and pulse duration (t) on the average dimple diameter (f), dimple distance (d), and depth (l) were studied using an L9 orthogonal array (OA). The analysis of variance (ANOVA) and multi-objective optimization techniques, including grey relational grade (GRG) and principal component analysis (PCA), were used to optimize laser process parameters on output responses. Moreover, the laser parameters must be optimized on geometrical parameters; otherwise, the laser parameters may negatively affect output performance. 1 METHODS In this study, the SS316L alloy was selected as the workpiece material. Surgical 316L stainless ssteel was purchased from SAILand its composition (wt %) are17.20 Cr, 12.60 Ni, 2.40 Mo, 1.95 Mn, 1Si, 0.03 C, 0.02 N, and balance Fe [16]. Nine samples with the length, width, and thickness of 25 mm, 25 mm, and 5mm, respectively, were used. The specimens were cleaned with acetone and immersed in an ultrasonic vibrator for 1min, and again, were cleaned with deionized water. The factors considered for the experiments are frequency (F) in kHz, average power (P) in Watts, and pulse duration (t) in nanoseconds. The factors are selected based on the preliminary experiments. Each factor has three levels, as seen in Table 1. OA was selected based on the degrees of freedom (DoF). DoF is used to determine how many runs are required to develop a parameter estimate. Each parameter estimate attracts one DoF from the total number of DoFs offered. The numbers of factors and levels were three and three, respectively. Thus, the multiplication of the number of factors with the difference between the number of levels and 1 provides DoF. The selected OAshould be higher than DoF; therefore, based on the relevant calculation, 3(3-1) provides six DoFs, and selected OA is larger than 6, that is, L9OA [3]. The average dimple diameter (f), dimple distance (d), and depth (l) were measured three times. 2 EXPERIMENTAL Laser dimples were generated on the specimen according to the L9 OA experimental design (Table 1). A fibre laser (CK-FB(A); Meera Laser Solutions) emitting a Gaussian beam at 1030 nm with the maximum output power and frequency of 20 W and 25 kHz, respectively, was used. The standoff distance was maintained at 0.7 mm. The quality of the laser beam highly depends on its polarization state. In practice, laser texturing is normally performed by the beam with circular and chaotic polarization for the fibre laser. The dimples are made with multiple pulses, and the laser jumps from dimple to dimple. The spot diameter of the laser beam on stainless steel was 150 ľm. The dimple depth and diameter Saravanan, K.G. – Thanigaivelan, R. Table 1. L9 OA and output responses Sample number F [kHz] P [W] T [ns] f [ľm] d [ľm] l [ľm] f STDEV d l 1 10 12 500 166.3 274.3 16.3 0.141 0.681 0.153 2 10 14 1000 161.2 265.4 15.4 0.833 0.200 0.200 3 10 16 1500 168.5 272.5 16.2 1.258 0.839 0.306 4 15 12 1500 173.9 250.2 17.3 0.709 0.404 0.100 5 15 14 500 171.7 252.1 16.8 1.159 0.153 0.231 6 15 16 1000 157.2 300.5 15.6 0.757 0.500 0.458 7 20 12 1000 134.9 335.3 13.9 0.586 0.252 0.361 8 20 14 1500 140.4 340.2 14.2 0.306 0.902 0.306 9 20 16 500 138.0 366.6 13.6 0.529 0.808 0.458 were characterized and analysed using a non-contact 3D surface profilometer (made by Taylor Hobson) and scanning electron microscopy. The time of laser hatching was recorded from the laser machine display. Fig. 1 illustrates the schematic of the micro-dimple geometry. Fig. 1. Schematic of the micro-dimples on a substrate 2.1 Grey Relational Analysis OA with the grey relational analysis (GRA) was used to determine the optimal machining parameters. In general, for GRG, each series is normalized by dividing the datainto the original series by using their average [17]. Letthe original reference and comparison sequences be xi *(k) and xi(k) , respectively, i = 1, 2,..., m; k = 1, 2,..., n, where m and n are the total number of experiments and observed data, respectively. Data pre-processing is used to convert the original sequence into an identical sequence. Several data pre­processing methods can be used in GRA, depending on the characteristics of the original sequence. If the target value of the original sequence is “the-larger-the-better”, then the original sequence can be normalized (Eq. (1)) as follows: xk°n()xk ()mi *ii xk()˜. i(1) xk°inxk() max()m ii If the purpose is “the-smaller-the-better”, then the original sequence can be normalized (Eq.(2)) as follows: xk°xk() max() *ii ()(2) ixk°inxk()xk˜. max()m ii After data pre-processing, a grey relational coefficient (GRC) can be calculated using the pre­processed sequences. GRC (Eq. (3)) are defined as follows: ..°. minmax ˜k., ()(3) ik.°. .() oimax where .oi(k) is the deviation sequence of the reference ** sequence xk(), xk()is the comparability sequence, 0i and . is the distinguishing coefficient, .. GRG (Eq. (4)) is a weighted sum of the GRC defined as follows: ˜.1n°i()(4) i.k,nk.1 where GRG .i represents the level of correlation between the reference and comparability sequences. 2.2 Multivariate Statistical Technique PCA is a multivariate statistical technique developed by Pearson [18] and adapted by Hotelling [19]. In this technique, several correlated variables are transformed into fewer uncorrelated variables. These transformed uncorrelated variables are calledprincipal components (PCs) and are the linear combinations of response variables. Following steps are involved in PCA[20]: Optimization of Laser Parameters and Dimple Geometry Using PCA-Coupled GRG Step 1. Formation of the decision matrix. PCA GRG is estimated using Eq. (9). An array of original response variables is nn constructed as the decision matrix as in Eq. (5). G˜°W.xi....(9) ij.m  i˜1ˆi˜1 ° x 11x 12x x 1n . ˜˜˜ 1323 ....... ......ˆ xxx x 2n 21 22 3 RESULTS (5) xxx x , n 31 32333 ˜˜˜°˜ Performance measures, such as the average dimple diameter, dimple distance, and dimple depth, were ˜ x xx x m mm 3 mn 1 2 where, X is the decision matrix of the response variables, n and m are the numbers of output characteristics and experimental observations, respectively. Output characteristics n = 3, m = 9, and decision matrix X is obtained as the GRC array of individual responses. Step 2. The correlation coefficient array can be acquired from the decision matrix and is expressed as Eq.(6). .Covx˜ˆ, x.ˆ .i.ˆi. °ˆ C˜°., (6) .x˜.. x° .ˆ..ˆ i. i where, (Cov(xi(a), xi(ß)) is the covariance of the sequences (xi(a)), (xi(ß)), sxi(a) is standard deviation (SD) sxi(a), and sxi(ß) = SD of sxi(ß). a = 1, 2, ..., n, ß = 1, 2, ..., n. Caß elements are mainly the correlation coefficients of the entire combinations of performance measures such as dimple diameter, dimple distance, and dimple depth. Step 3. Determination of Eigenvalues and vectors. Eigenvalues and vectors are estimated from Caß by using Eq. (7). E˜C°I, (7) ik....km.ˆ where, dk is the Eigenvalue such that °n.k. ˜n, k˜1 and Eik is the Eigenvector corresponding to Eigenvalue dk. Step 4. Estimation of PCs. PCs are estimated using Eq. (8) and are arranged in the decreasing order of variations captured by them. Hence, the highest variability in the data is generally explained using the first principal component. nPmk˜.xm°.i.Eik,(8) i˜1 where Pmk is the first PC. considered. An increase in the average dimple diameter leads to the enhancement of lubrication storage for tribological applications and cell adherence in biomedical applications; thus, “the-larger-the-better” is considered the optimum condition. A decrease in the dimple depth and distance is effective to minimize the friction and wear; hence, “the-smaller-the-better” condition is used for the analysis. The original sequence is normalized using Eqs. (1) and (2) [21]. Subsequently, the deviation sequence is determined by subtractingthe comparable sequence from the ideal sequence. GRC for output responses are calculated using Eq. (3) (Table 2). The PCAprocedure is adapted to calculate the optimized weights. The GRC of the average dimple diameter, dimple distance, and depth are used as the elements of the decision matrix, which is used to calculate the correlation coefficient array and Eigenvalues using Eqs. (6) and (7), respectively. Table 3 presents the Eigenvalues and proportions for PCs. The first, second, and third PCs, that is, average dimple diameter, dimple distance, and dimple depth, respectively, account for 34.6 %, 34.5 %, and 31 % of variations, respectively, in GRG. Therefore, the accountability proportions of the first, second, and third PCs of 0.346, 0.345, and 0.310 are considered the optimum weights for the average dimple diameter, dimple distance, and dimple depth (Table 4). Table 2. Calculated GRC value Sample number f [ľm] d [ľm] l [ľm] 1 0.7196 0.7072 0.4066 2 0.6056 0.7929 0.5068 3 0.7831 0.7230 0.4157 4 1.0000 1.0000 0.3333 5 0.8986 0.9684 0.3663 6 0.5387 0.5364 0.4805 7 0.3333 0.4061 0.8605 8 0.3679 0.3927 0.7551 9 0.3520 0.3333 1.0000 GRG was calculated by taking the average of GRCs matching to each process response by using Saravanan, K.G. – Thanigaivelan, R. Eq. (9). The overall estimation of multiple response process parameters was based on GRG (Table 5), and the experiment with the highest GRG is optimal among all the runs. The GRG for each L9 OA experiment was used to optimize laser process parameters (Table 5). Subsequently, the GRG of each combination was ranked as per value, and a set of optimal machining parameter settings was obtained based on the highest GRG peak value of 0.2642 with 15 kHz (frequency), 12 W (average power), and 1500 ns (pulse duration). The result indicated that experiment sample number 4 (Table 5) shows desirable responses among the nine experiments. Table 3. Eigen values and proportions for PC PC Eigen values Proportion [%] First 0.1694 94.3 Second 0.0077 4.3 Third 0.0025 1.4 Table 4. Accountability proportion of output responses Output Eigen vectors Contribution (Wj) parameters PC1 PC2 PC3 f (ľm) 0.586 0.316 –0.746 0.346 d (ľm) 0.588 0.468 0.660 0.345 l (ľm) –0.557 0.826 –0.088 0.310 Table 5. Ranking of parameter combination Sample number F [kHz] P [W] T [ns] Gi Rank 1 10 12 500 0.2059 5 2 10 14 1000 0.2131 4 3 10 16 1500 0.2160 3 4 15 12 1500 0.2642 1 5 15 14 500 0.2524 2 6 15 16 1000 0.1732 8 7 20 12 1000 0.1739 7 8 20 14 1500 0.1655 9 9 20 16 500 0.1821 6 Fig. 2 shows the micro-dimples machined for the optimum combination of parameters: 15 kHz (frequency), 12 W (average power), and 1500 ns (pulse duration). The pile-up phenomenon around the dimples (Fig. 2a) contributes to limiting the contact surface, leading to decreased wear. Fig. 2b shows the magnified view of micro-dimples and depicts the rough surface and re-solidified/re-cast areas. The re-solidified and re-cast layers on the micro-dimple resulted from the medium scanning speed, where material heat absorption decreased, leading to incomplete melting. a) b) Fig. 2. Micro-dimple machined at optimum laser parameters, a) micro-dimple, b) a magnified view of the micro-dimple The most significant factors of machining parameters are determined by estimating the difference between the maximum and minimum values of mean GRG.The values are 0.2299, 0.2147, and 0.2168 for the frequency, average power, and pulse duration, respectively. The result showed that the frequency (0.2299) has the most decisive influence on multi-performance characteristics. In the sequence, the order of significance of controllable factors to multi-performance characteristics in the laser process is as follows: frequency, pulse duration, and average power. An increase in the pulse frequency improves laser spot extension leading to large and deep micro-dimples. At the frequency of 15 kHz, the pulsed laser beam exposed to the particular area of the workpiece causes heating and melting. With an increase in the pulse duration, the material exposure time increases, Optimization of Laser Parameters and Dimple Geometry Using PCA-Coupled GRG and hence, heat transfer to the workpiece increases. The pulse duration of 1500 ns contributes to the generation of wide and deep dimples. 4 DISCUSSION Fig. 3 presents the mean effect plot for output performance. With the frequency increase, output performance increases. The pulse frequency is the number of pulses per unit of time. With the increase in the pulse frequency, the number of pulses per second also increases. When a pulsed laser beam focuses on the SS316L workpiece surface, the required area on the substrate heats, melts and/or evaporates. Moreover, overlapping between the laser spots results in material removal. Thus, an increase in the pulse frequency causes overlapping between the laser spots, resulting in highly prominent diameter micro-dimples. The further increase in the frequency and improper ejection of molten materials hinder micro-dimple formation. The increase in laser power leads to a decrease in output performance. The increasing of power attributes to material removal, and the inadequate removal of molten materials reduces output performance. The Fig. 4. a) Micro-dimples, and b) a magnified view of the dimple Fig. 5. Loading and unloading graphs of Vickers hardness for a) 10 mN, and b) 100 mN Saravanan, K.G. – Thanigaivelan, R. important factor that significantly affects the micro-dimple quality is the interaction time between the laser beam and material. During interaction time, heat absorption of the material decreases, resulting in unfinished melting and the evaporation of the material surface, which leads to a decrease in the dimple diameter, distance, and depth. Fig. 4 illustrates the machined micro-dimples. The factor pulse duration exhibits an increasing trend in output responses. With an increase in the pulse duration, the laser exposure time on materials increases, contributing to an increase in output responses. Fig. 5 presents the loading and unloading graphs of Vickers hardness. The force was steadily lowered from 10 mN to 0 mN and 100 mN to 0 mN. The load and penetration depth of the indenter were continuously measured under the load-unload conditions. The nano-indentation test showed the hardness of 406.85 HV and 341.15 HV for 10 mN and 100 mN loadings, respectively. The Vickers hardness of SS316L was 140 HV in the non-laser irradiation area, and hardness increased almost three times. The arrow mark presented in Fig. 5b shows the measurement positions on the dimple for the Vickers hardness test. 4.1 ANOVA for PCA–GRG ANOVA is used to determine the most influencing parameter and to estimate the contribution percentage of laser process parameters [22] and [23]. ANOVA is a statistical tool employed to obtain the experimental results and to establish the performance of a group of analysed parameters. The total variance of an observed value can be calculated using the sum of squares (SS). Moreover, the sample variance is considered the mean square, which is obtained by dividing SS with the respective DoF. The F-value is the ratio of two variances and shows how far the values are scattered from the mean value. Larger F-values represent higher dispersion within the group. As per the ANOVA(Table 6), the average power is the most influential factor, contributing 86.40 % to the performance measures, followed by the pulse duration [ns], and frequency [kHz], which contributes11.66 %, and 1.77 % to these measures. It is evident from the literature that the ablation mechanism is determined by applied power, and pulses have the least significance [24]. According to the comparison of the analytical mean results of PCA-GRG and ANOVA, the frequency and average power are the most significant parameters for dimple creation. This result variation was obtained because within the mean effect calculation, the difference between the two groups was considered, and in ANOVA, the variation between the sample means and the variation within each sample was considered. 5 CONCLUSIONS In this study, micro-dimples were fabricated on SS316L, and the effects of laser process parameters such as the frequency, average power, and pulse duration on the average dimple diameter, dimple distance, and dimple depth were studied by using L9 OA. The performance measures were optimized using PCA-coupled GRG. The following conclusions are drawn from the study. 1. The micro-dimples were successfully fabricated on the SS316L samples of 25 mm × 25 mm × 5 mm. 2. The optimal laser parameter setting for suitable micro-dimple generation for tribological applications is 15 kHz (frequency), 12 W(average power), and 1500 ns (pulse duration). 3. The most significant factors influencing micro-dimple generation are frequency and average power. 4. In the sequence, the order of significance of controllable factors to the multi-performance characteristics in the laser process is as follows: frequency, pulse duration, and average power. 5. With the increase in laser power, output performance decreases. 6. The development of a research model for predicting the number of micro-dimples for Table 6. ANOVA results Factors F P t Error Total DoF 2 2 2 2 8 SS 0.0000901 0.0043769 0.0005911 0.0000074 0.0067852 MS 0.00004505 0.00218845 0.00029555 0.0000037 0.00084815 F-value 12.18 591.47 79.88 1 684.53 Percentage contribution [%] 1.7786 86.4060 11.6691 0.1460 ~ 100 Optimization of Laser Parameters and Dimple Geometry Using PCA-Coupled GRG 531 given area will be considered for further studies using the RSM model. 6 ACKNOWLEDGEMENTS This manuscript was edited by editingindia. 7 REFERENCES [1] Fellah, M., Labaďz, M., Assala, O., Iost, A., Dekhil, L. (2013). Tribological behaviour of AISI 316L stainless steel for biomedical applications. Tribology - Materials, Surfaces & Interfaces, vol. 7, no. 3, p. 135-149, DOI:10.1179/1751584X 13Y.0000000032. [2] Bekmurzayeva, A., Duncanson, W.Jr., Azevedo, H.S., Kanayeva, D. (2018). Surface modification of stainless steel for biomedical applications: Revisiting a century-old material. 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Experimental investigation of edm processing parameters on machining Al6063-12% sic-5% gr using response surface methodology. High Temperature Material Processes: An International Quarterly of High-Technology Plasma Processes, vol. 18, no. 1-2, p. 27-43, DOI:10.1615/HighTempMatProc.2015014659. [24] Zhao, W., Wang, W., Jiang, G., Li, B.Q., Mei, X. (2015). Ablation and morphological evolution of micro-holes in stainless steel with picosecond laser pulses. The International Journal of Advanced Manufacturing Technology, vol. 80, no. 9, p. 1713­1720, DOI:10.1007/s00170-015-7145-8. Optimization of Laser Parameters and Dimple Geometry Using PCA-Coupled GRG Mechanical Characterization and Structural Attributes of Biohybrid Composites Derived Using Hemp, Bamboo, and Jute Fibres: an Alternative Approach in the Application of Natural Fibres in Automobile Parts Rajmohan, B. – Arunachalam, K. Rajmohan Bose – Arunachalam Kandavel MIT Campus Anna University, India In the present work, three natural fibres, namely jute, hemp and bamboo have been hybridized with seashell powder and polypropylene resin as biohybrid composites. Nine samples are considered for this study with various weight propositions of bamboo, hemp, and jute. The mechanical characteristics, such as the flexural, impact, and tensile strength of nine samples, are compared, and the Sample 9 shows very good results; the obtained flexural, tensile strength and impact energy of Sample 9 are 239.36 MPa, 47.84 MPa, and 18.33 J, respectively. The main reason for this is the presence of jute material and layering pattern; Sample 9 contains 60 % Jute, 20 % hemp, and 20 % bamboo; the percentage of jute is high compared to the other eight samples. Furthermore, morphological analysis and thermogravimetric analysis (TGA) have been carried out with Sample 9. While comparing the properties of with the existing dashboard material properties, they show more desirable values and thus, the compositions of Sample 9 material can be used for various vehicle parts. When the experimental results are compared with the finite element analysis (FEA) results, the experimental results match with the FEA results, and few variations are noticed. Keywords: thermogravimetric analysis, morphological analysis, jute, hemp, bamboo, finite element analysis Highlights • This Investigation has considered three natural fibres (hemp, bamboo, and jute) with seashell powder and polypropylene resin, and they are hybridized in biohybrid composites. • Experimentally, its potential is investigated through various mechanical characteristics, such as tensile, flexural strength, and impact energy. • The authors have also validated the reported results with FEA modelling. 0 INTRODUCTION Naturally, derived cellulose fibres have gained significant attention due to their diverse applications in the engineering disciplines, and especially in end uses, such building materials and structural parts of motor vehicles for which there is a need for a lightweight material. Low cost followed by lesser tool wear at the time of processing are some of the major advantages seen during the implementation of plant fibres, as well as the ease of recycling an environmentally viable and friendly material. Over 1000 different plant species bearing cellulose-based usable fibres exist [1]. There is a growing consciousness of environmental sustainability and a profound interest among researchers in using natural fibres (NF) like bamboo, pineapple leaf, hemp, kenaf, banana, jute, coir, sisal, oil palm, and flax as green reinforcements of polymer composites for minimizing the overall implementation and of synthetic fibres, such as Kevlar, glass, and carbon [2]. The recent decade has seen the rise of the utilization of NF as a reinforcement concerning composite materials for replacing glass fibres. The automotive industry remains one of the promising and prominent users of naturally derived composites in their fabrication and interior applications, especially in the making of trunk liners and door panels for vehicles [3]. It is estimated that the increase in the utilization of NF as an alternative for several automotive components might reach about 54 %, since European and US car manufacturers have implemented Environmental Directives. Several US-based automotive have embraced using natural materials: about 1.5 million vehicles are already using vegetable fibres such as jute, hemp and kenaf as reinforcement of thermoplastic and thermosetting polymers [4]. Under this particular aspect, automobile parts like bumpers, panels, and other related automotive components are produced using fibres or with other forms of bio-thermoplastic as well as hybrid composites, which are already in use [5]. Composite materials involve a combination of two or even more differently derived materials for their effective reinforcement in industrial application. They are broadly used in many major industries, including automotive, aerospace, marine and electric, which require light weight-to-strength ratios [6]. Bamboo (Bambusoideae) is a group of gigantic grass plants. It is estimated that there are about 2000 species of bamboo around the world [7]. There is a steady rise in the marketing of bamboo-based products as well as their manufacturing has increased rapidly. This is especially attributed due to its effective application from being an unwanted bamboo particulate which is concentrated to develop micro-bamboo filler derived polymer composite; this happens to serve as a better opportunity in replacing it effectively as an NF for enhancing materials’property [8]. Furthermore, the plant is well-known for its excellent mechanical properties (MP) based on comparative mass and the distinct attributes of its unique physical structure [9]. The developed brake pads using seashell powder gave a better friction coefficient as the optimal value (0.48) falls within the class G (0.45 to 0.55) type of brake pads recommended for use by the Society of Automobile Engineers (SAE) [10]. It was found that poor binding with epoxy/natural filler, agglomeration issue under a higher filler weight %, and also susceptible to increased moisture content [11]. Several types of research studies have analysed its effect on the recycling of polypropylene (PP), which is known as the most widely used matrix in terms of composite materials reinforced alongside natural fibres [12]. For instance, a study that concentrated on both the mechanical and rheological properties has shown that pure PPremains to be practically observed as unaffected while performing the first four recycling cycles. Also chemical treatments involving alkali, silane, and acid-based treatments have appeared to be the most utilizedapproaches in developing a fibre and natural filler [13]. Jute, which remains in the top the tables in terms of its usage as vegetable fibre, is grown in tropical countries like India, China, Bangladesh, Thailand, and Nepal. These countries collectively contribute 95% of global jute fibre production [14]. As the majority of studies focus on jute fibre/non-degradable polymer composites, research about jute/biodegradable polymer-based composites has been found to be quite limited [15]. For the hemp plant, the fibres contained within the tissues facilitate holding the plant in an erect position. Hence, the fibres exhibit strength as well as stiffness. Both of these factors make hemp quite suitable for the reinforcement of composite-based materials [16]. In this study, physical, chemical and MP of NF’s properties observed [17] and [18]. The chemical, physical and MP of polymers properties observed [19]. Fibre type is commonly categorized based on its origin: plant, animal, or mineral. From these types, plant-based fibres are the most suitable to be used in composites with structural requirements. Furthermore, plant fibres can suitably be grown in many countries and can be harvested within short periods. The focus, for example, in Europe, has been on flax, whereas hemp, jute, ramie, kenaf, and sisal have been of greater interest in Asia. Generally, higher performance is achieved with the varieties of higher cellulose content and with cellulose microfibrils aligned more in the fibre direction. It tends to occur in blast fibres (e.g., flax, hemp, kenaf, jute, and ramie) that have higher structural requirements in providing support for the plant’s stalk [20]. Researchers [21] have found that with the addition of chemically treated wood flour in PP(polypropylene), sustained improvement is obtained in composite’s tensile strength. The research study evaluates the overall environmental impact of alkaline-treated bamboo, jute, and hemp fibre on a PP matrix. Flexural, impact, and tensile characteristics, scanning electron microscope (SEM), Energy Dispersive X-ray (EDX), TGA and FEA analyses have been investigated to develop verified weight proportions and layering patterns of bio hybrid composites for applications suitable for automobile parts. 1 EXPERIMENTAL PART 1.1 Fabrication of Specimen Compression moulding is a form of moulding that requires preheated moulding material that is initially mounted on an open, heated, mould -etched cavity. At the Indian Institute of Technology Madras, compression moulding is used to create these composites. The composites are made up of NF and PP in the ratio of 1:4. The weight composition of the composite produced are 100 grams of fibre, 400 grams of PP resin, and 5 grams of seashell powder. The conditions involved in fabricating the specimen are: time: 4 hours, stirring time: 45 minutes, pressure: 25 bar, and temperature: 75 şC. The dimensions of the composite shape are 300 mm × 300 mm × 4 mm (length × width × thickness). The bio hybrid composites are fabricated into nine different weight proportions and layering patterns. The weight composition is illustrated in Table 1. Four layers of bio-hybrid composite are fabricated at a defined orientation (0° & 90°). These three fibres (hemp, jute, and bamboo) was purchased from Go Green Products, Chennai. PP resin was purchased from Vasavibala resins (P) Ltd, Chennai. Seashells are collected from the seashore and powdered using a ball-milling machine. Table 1. Sample weight distribution Sample Hemp [%] Bamboo [%] Jute [%] Sample 1 40 30 30 Sample 2 30 40 30 Sample 3 30 30 40 Sample 4 50 25 25 Sample 5 25 50 25 Sample 6 25 25 50 Sample 7 60 20 20 Sample 8 20 60 20 Sample 9 20 20 60 1.2 Chemical Treatment The purpose of chemically treating the NF is primarily the removal of lignin, waxy substances, pectin, and natural oils that coat the external portion of the fibrous cell wall. This initial treatment exposes fibrils, thereby giving a rough topographical surface for the fibre. Bleachingis carried out using sodium hydroxide (NaOH) as it is commonly used chemical in cleaning/ bleaching plant fibres’ efficiently. Also, the chemical modifies the finer structural characteristics in native cellulose I/II via alkalization [22]. Fibre treatments involving silane and alkali require treatment for increasing adhesion between hydrophilic groups endowed in NF with that of the hydrophobic epoxy matrix [23]. Interfacial bonding in the case of the fibre and matrix is found to have improved effectively via chemical treatment using a coupling agent [24]. In this study, alkaline treatment has been done with 5 % NaOH; the material is dipped in it for 72 hrs and dried [25] and [26]. 1.3 Mechanical Characterization Auniversal testing machine (Instron 5567, Shakopee) has been employed to perform mechanical attributes, and elongation is performed under the breakage of composites under different loadings. Composite samples are cut into a rectangular shape of 246 mm × 29 mm × 4 mm with a band saw machine for tensile testing, which has been carried out as per ASTM D3039 standard involving cross-head speed 5 mm/ min with gauge length spanning 50 mm. Data on loading, displacement, and strains are acquired. The impact in terms of strengths from derived composites under varied loading are estimated using the FIT­300-D machine. The samples are then cut into a rectangular shape of 64 mm × 12.7 mm × 4 mm using a band saw. V-notched specimens were further tested as per ASTM D256. Appropriate pendulum hammers of over 10 kJ are mounted. The analysis tool involves the calibration of energy followed by accurate determination showing the exact amount in terms of impact energy [J/m] involved for the tests. The energy needed for breaking composite specimens in terms of toughness and average impact energy is determined. Flexural properties of composites are evaluated by involving bending (three-point) using a universal testing machine, as per ASTM D790 standards. Furthermore, they are cut as per the standard dimension of 250 mm × 25 mm × 4 mm. The machine is operated at a cross-head speed of 1 mm/min with a support span-to-depth ratio involving 16:1. The existing material mechanical properties (tensile, flexural and impact) were obtained as per the same testing procedure followed by the experimentally tested mechanical properties of the nine samples. 1.4 Scanning Electron Microscopy (SEM) SEM is facilitated for observing the morphological attributes of the experimental composites comprising of montmorillonite (MMT) based inorganic load. This form of analysis is essentially used for studying the surface characteristics of the composites via constructing three-dimensional images under high resolution with excellent depth in the field and no loss in image sharpness. The morphological features of the fractured sample from the composites are determined using QUANTA 200F – SAIF - IIT Madras. 1.5 Energy Dispersive X-ray (EDX) EDX is an X-ray approach used to determine the elemental composition of materials. AQUANTA200F – SAIF-IIT Madras SEM instrument has been used to perform this elemental analysis. 1.6 Thermogravimetric Analysis (TGA) The TGA of polymers serves as a very effective technique for assessing thermal stability. Similar approaches employed are DGC, DTA, and derivatographic analysis [27] for a comparative study, especially for assessing the thermal behaviour of related categories of polymer samples, with each of its samples analysed via one or more of such methods involving identical experimental attributes. For instance, TGA is performed in air with an oxygen-free nitrogen composition. The process is Rajmohan, B. – Arunachalam, K. carried out under varied thermal conditions/different heating rates. It is imperative to assess the polymeric samples under different thermal analyses under a given method based on numerous aspects [28]. The amount, as well as material particle size, is examined these factors influence the nature of the thermogram. Thermogravimetric readings monitor the speed of the recorder by noting modifications in terms of weight and shape of sample containers which impact thermogravimetric outcomes. The heating rates of the sample and the ambient atmosphere during the time of analysis are assessed. The information achieved from TGAis found to be complementary to the other methods mentioned above. The study employed TGA analysis via the Universal V4.5A TA instrument in CLRI, Chennai. 1.7 Finite Element Analysis (FEA) This work [29] has focused on numerical modelling, and verification of the experiment results is performed with the compression of axially compressed honeycomb cores. The explicit standard simulation has offered good results for the first phase of the test, and the force calculated by the simulation is very close to the experimental tests. The formula used for density calculation is: Density = .(A×%A)+ .(F)(B×%B) + .(F)(C×%C)+ .(F)(D×%D). (1) The density calculation is carried out using the fibre weight percentage and resin weight percentage. In this formula, . stands for density, A refers to matrix material density. B, C and D denote the densities of each of the fibres. Semi-empirical models have been successfully used to describe the characterization of composite materials. Rule-of-mixture models are commonly used to predict Young’s modulus [30]. EC = EFEM / (EFVM + EMVF). (2) In this equation, EC, EF, and EM are Young’s modulus for the composites, matrix, and fibres respectively; VF and VM are the volume fractions of the fibres and matrix, respectively. 2 RESULTS AND DISCUSSION The 5 % sodium hydroxide alkaline treatment of NF has improved the biohybrid specimen’s mechanical characterization, although the aforementioned treatment wipes out the dust and other impurities of the fibre. It is more convenient to obtain interfacial adhesion bonding among reinforcement and matrix. The reinforcement and matrix parameters have greatly influenced the mechanical attributes and performed an influential role in fabricating the biohybrid specimens. Furthermore, the seashell powder is acknowledged because it contributes to the improvement of the biohybrid specimen’s mechanical properties and also helps to minimize the voids and cracks in the specimen. The experimentally tested mechanical properties of the nine samples and the existing material are given in Table 2. The properties of the existing material are compared with the samples obtained with high values [31]. Table 2. Mechanical strength of composite samples Sample Tensile Flexural Impact strength [MPa] strength [MPa] energy [J] Sample 1 31 167.99 13.16 Sample 2 28.79 107.14 14.16 Sample 3 28.53 150.94 11.33 Sample 4 24.53 94.05 21 Sample 5 33.20 132.42 15.16 Sample 6 40.59 184.23 16.83 Sample 7 34.30 132.73 14.83 Sample 8 35.82 178.02 18.33 Sample 9 47.84 239.36 18.33 Existing material 18.1 68.2 3.6 2.1 Tensile Strength The results assessments of various mechanical characteristics of fabricated composite (jute, hemp, bamboo) with regard to tensile strength are summarized in Table 2 for better comparison. Significantly, it is revealed that the joint exists with dissimilarities in aspect ratio, and the varied material fibres affect the inborn property; thus, mechanical characteristics increase/decrease, when applied individually. It is observed that the tensile strength of Sample 9 appears high comparedto other sample data. Stress rises linearly by concerning the rises in strain of composites. The least tensile strength of 24.54 MPa is obtained in Sample 4 which has 50 % hemp, 25 % bamboo, and 25 % jute, whereas Sample 9 has 20 % hemp, 20 % bamboo, and 60 % jute. This shows the highest value of 47.85 MPa. Thus, by concerning the tensile strength, the maximum value is obtained in the combination in which the presence of jute is the highest as well as the layering pattern of the biohybrid composite. 2.2 Flexural Strength The flexural resistance observed from the composites is shown in Table 2. The flexuralstrengths for samples establishes from the linear portion of the curve via defining load and equivalent displacement, which is also indicated in Sample 9 to exhibit the highest value compared to other composite samples; this sample has the fibre content of 20 % hemp, 20 % bamboo, and 60 % jute. Sample 4 has a fibre content of 50 % hemp, 25 % bamboo and, 25 % jute. Sample 4 has the lowest value, at 94.05 MPa, which is not even half of the maximum value obtained. 2.3 Impact Energy The test is achieved by assessing the impact of capability under different composites. Impact energy aid in determining the material’s toughness. It is regarded as one of the critical factors measuring the ability to absorb energy while the sample undergoes plastic deformation (PD). Brittle materials, for instance, exhibit low toughness as an effect of a smaller degree of undergoing PD. In the case of NF, it shows greater fibre/matrix strength as it does not facilitate energy being absorbed in interfaces. This loss of energy is established via the Charpy impact test machine. The absorbed energy under each specimen when impacted by heavy blow, and the resultant data are summarized in Table 2. Resin toughness compared to that of fibre strength as well as stiffness observed is regarded as a major parameter that influences composite’s impact-resistant properties. It is seen that Sample 4 has exhibited high impact energy upon comparison with other composites; the energy absorbed is 21 J. Sample 4, which shows a high impact value, has the fibre content of 50 % hemp, 25 % bamboo, and 25 % jute. Sample 9 has the second-highest value at 18.83 J. 2.4 Comparison of Composite Material with Existing Material The highest tensile, flexural, and impact values obtained from the experiment are compared with the existing material for car dashboards are shown in Table 2. The major proposal of this study is to suggest a composite material made of NF for the manufacturing of various automobile interior parts. Thus, the car dashboard is selected as a proposal, and the properties are compared. The tensile strength of the existing material is 18.1 MPa, but the highest value obtained in Sample 9 is 47.85 MPa, which is 29.75 MPa high. Thus, the proposed composite possesses a value 2.6 times more than the existing value. The flexural strength of the existing materials is 68.2 MPa, but the highest value obtained in Sample 9 is 239.36 MPa, which is 171.16 MPa high. Thus, the proposed composite possesses a value which is 3.5 times more than the existing value. The impact energy of the existing material is 3.6 J, but the highest value obtained in Sample 4 is 21 J, which is 17.4 J higher than the existing material. Thus, the Sample 4 possesses a value which is of 5.83 times higher than the existing value. 2.5 Morphological Analysis via SEM Morphological analysis has been performed via SEM. Sample 9 has exhibited better MP, which is evident from the observed samples selected for the morphological analysis. The prediction of macroscopic behaviour of composite materials is of fundamental and practical importance, as new composites are being developed continually for the automotive as well as aerospace industries, electronic packaging, thermal insulation, and many other applications [32]. There is an improvement in the interfacial adhesion between the matrix and coating; hence, the mechanical properties are attributes of the high strength cellulosic composite. SEM observations have allowed highlighting the different morphology of the surfaces [33]. Adetailed analysis of the microstructure was carried out; it highlighted the predominant role of fibre orientation in the MP of composites [34]. It was found that chemical treatment results in composites with better MP’s compared to the composites without any treatments [35]. Fig. 1 displays a micrograph of a fractured specimen of tensile, flexural, and impact test of Sample 9, which shows improved MPrelated to other samples. Alkaline treatment of the composites aids in better interfacial bonding, which in turn improves the mechanical strength. Pull-out phenomena of the fibres are presented in the image. The SEM images reveal some voids due to the fibre pull-out, and more breakages of fibres. SEM images Fig. 1b to d indicates better fibre-matrix interaction between the fibres and the polymer matrix. Fig. 1a depicts the SEM image of the parent material; it clearly shows the fibre direction. It also demonstrates a very good bonding between the resins and fibre, thus indicating a better mechanical bonding with the fibre-polymer matrix. Fig. 1b Rajmohan, B. – Arunachalam, K. Fig. 1. SEM image of a) Specimen 9 top surface, and b) fractured Specimen 9 of tensile test, c) fractured Specimen 9 of impact test, and d) fractured Specimen 9 of flexural test d) delineates the SEM image of a fractured specimen during a tensile test where the fibre pull out position is visible in the image. Fig. 1c illustrates the SEM image of a fractured specimen of impact test, and Fig. 1d shows the SEM image of the fractured specimen of the flexural test. SEM images show that there are no voids, blowholes, or cracks on the top surface and in the inner layers. These figures reveal that the mixture of the resin and fibre is uniform in all the biohybrid composites. 2.6 Energy Dispersive X-ray (EDX) Fig. 2a represents the distribution of fibres and particulate reinforcement from observed composite samples; Fig. 2b depicts the energy dispersive X-ray (EDX) spectroscopy of composites material. From the observed EDX analysis, it can be seen that the particles are primarily composed of component mixtures, such as Ca, Cl, and C. There are numerous trace elements, including K, Ca, and Si, present in the samples. The EDX comprises a unique class of fillers that are effectively applicable for polymer matrix as a result of their fine dispersion, inertness, homogeneity, chemical stability, and low water absorption. The presence of Ca was reported as high, as a result of seashell powdered composite elements deposited in composite samples. Fig. 1a shows that the seashell powder is finely distributed throughout the sample Fig. 2. Composite material Sample 9, a) EDX image, and b) EDX graph SEM, and it helps to increase the mechanical strength of the composite material. 2.7 Thermogravimetric Analysis (TGA) A special TGA technique has been developed for quantifying the composite mass percentage. TGA curves derived from additives, fibres, and polypropylene are assigned for a composite fraction. The heating rate is fixed for thermal degradation of single components via using dynamic as well as isothermal segments. Sample 9 is thermogravimetrically analysed with the existing material to find the percentage of weight loss concerning increasing temperature. The three major weight loss regions are represented in Fig. 3. Fig. 3a displays the TGA results of Sample 9, and Fig. 3b shows the TGAof the existing material. Table 3 gives the peak temperature at various mass loss percentages and the average temperature is calculated by the obtained values. Sample 9 can withstand even at 451 °C, with 50 % of weight loss and the working temperature in real-timeis much less than the calculated temperature. The existing material shows an average temperature of 536.3 °C, and Sample 9 shows an average temperature of 430.6 °C, which is 19.4 % less than that of the existing material. The major reason for this reduction is that the composite is made of NFs. 2.8 Comparison between the Experimental results and FEA As per the ASTM standard tensile, flexural and impact test specimen size, the surface geometry is created using Creo. The created geometry is imported into the ANSYS workbench, and solid jet 4 node 285 element has been chosen for composite laminates. The properties of isotropic fibre-reinforced composites are a) b) Fig. 3. TGA; a) Sample 9, and b) existing material Table 3. The peak temperature at various mass loss percentages Peak temperature [°C] and (weight [g]) Mean temperature Wt. loss [%] 10 20 30 40 50 60 [°C] Sample 9 360.6 (6.69) 417.3 (5.95) 429.0 (5.2) 437.6 (4.46) 452.0 (3.71) 487.0 (2.975) 430.6 Existing 517.0 (4.361) 527.0 (3.94) 534.6 (3.45) 540.7 (2.95) 546.0 (2.46) 552.7 (1) 536.3 Table 4. Boundary conditions Properties Composite density [g/cmł] Young’s modulus [N/mm˛] Poisson’s ratio Tensile strength [MPa] Flexural strength [MPa] Impact energy [J] Sample 9 0.99 7.57×103 0.3 47.84 239.36 21 Existing car dash board 1.03 7.27×103 0.3 18.1 68.2 3.6 theoretically calculated for Sample 9 and the existing material. They are given in Table 4. 2.8.1 Tensile Strength Analysis Fig. 4 and Table 5 show tensile strength analysis of the existing material and Sample 9. Table 5. Tensile strength comparison Tensile strength [MPa] Test conducted Existing material Sample 9 Experimental work result values 18.1 47.84 FEA work result values 18.57 44.73 Variation 0.47 3.11 Variation % D 2.56 I 6.72 The experimental value and the FEA values are within 5 % of variation. As mentioned earlier, Sample 9 exhibits tensilestrength, which is 64.1 % higher than the existing material. Experimental values match with the FEA results. FEA results of Sample 9 have the tensile strength of 44.73 MPa, and the experimental result is 47.84 MPa, which is a 6.72 % increase from the FEA result. The FEA results of the existing material show the tensile strength of 18.57 MPa and the experimentalresult is 18.1 MPa, which is a 2.56 % decrease from the FEAresult. In Tables 5, 6, and 7. D stands for decrease and I for increase. 2.8.2 Flexural Strength Analysis Fig. 5 and Table 6 show the flexural strength analysis of the existing material and Sample 9. Table 6. Flexural strength comparison Flexural strength [MPa] Test conducted Existing material Sample 9 Experimental work result values 68.2 239.36 FEA work result values 71.62 263.38 Variation 3.42 24.02 Variation % D 4.89 D 9.55 Fig. 4. Tensile strength analysis, a) existing material, b) Sample 9 a) b) Fig. 5. Flexural strength analysis, a) existing material, b) Sample 9 a) b) Fig. 6. Impact energy; a) existing material, b) Sample 4 When the experimental values and FEAvalues are compared, for the existing material, the experimental value of the existing material is 4.89 % less than the FEAresults, where as in Sample 9, there is a decrease of 9.55 % compared to the FEAresults. As mentioned earlier, Sample 9 exhibits flexural strength that is 150.96 % higher than the existing sample material. 2.8.3 Impact Energy Analysis Fig. 6 and Table 7 shows the impact energy analysis of the existing material and Sample 4. Table 7. Impact energy comparison Impact energy [J] Test conducted Existing material Sample 4 Experimental work result values 3.6 21 FEA work result values 4.02 24.27 Variation 0.42 3.27 Variation % D 11.02 D 14.44 The experimental value and the FEA values are compared. It is clear that there is a decrease of 11.02 % in the existing material and 14.44 % in Sample 4. As mentioned, Sample 4 exhibits impact energy that is 5.83 times higher than that of the existing material. Experimental values match the FEAresults. The FEA results of sample 4 show the impact energy of 24.27 J, and its experimental result is 21 MPa, whereas the FEA result of the existing material is 4.02 J, and the experimental result is 3.6 J. 3 CONCLUSIONS The composite samples consist of jute, bamboo, and hemp, and they are fabricated. Hybrid composites are subjected to tensile, impact, and flexural testing. Results obtained are compared with the existing material. All the composites exhibit a high value compared to the existing material. Among the nine samples, Sample 9 (20 % hemp, 20 % bamboo, and 60 % jute) provides the highest values. Thus, for further Rajmohan, B. – Arunachalam, K. morphological analysis, TGA and FEA analyses are performed with Sample 9, and the results are compared with the existing material results. Consequently, the Sample 9 composite will be the best replacement for the existing material and the same can be utilized for the manufacturing of various vehicle parts. Based on the results, the major conclusions identified are: • The tensile strength of Sample 9 composite at 20 % hemp, 20 % bamboo, and 60 % jute wt.% fibre, compared to the existing material, is increased by 64.1 % (47.84 MPa) more compared to the existing material (18.1 MPa). These values are high at 29.74 MPa for Sample 9. The unique property of this sample is the presence of high wt.% of jute. • The flexural strength of Sample 9 is 239.36 MPa, which is 150.96 % higher than that of the existing material (68.2 MPa). • The impact energy of Sample 9 is five times higher than that of the existing material, and the value for sample 9 is 18.33 J, whereas it is 3.6 J for the existing material. Thus, it can be concluded that the material can withstand sudden impact without any deformation. This property is essential for the materials of automobile parts. • Morphological analysis has been carried out to better understand the structure formation and interfacial bonding between the materials. • TGAresults show that the existing material has an average temperature of 536.3 °C, and Sample 9 has an average temperature of 430.6 °C, which is 19.4 % less than the existing material. The major reason for this reduction is that the composite is made of NFs. The working temperature of the interior and exterior parts of the automobiles is below 460.6 °C and as a result, the material can be used to manufacture automobile parts. • The FEA results are consistent with the experimental results which show that FEA is the correct tool for testing and evaluating the mechanical behaviour of hybrid structures. • The results indicate the possibilities of using composites made from NF, such as jute, hemp, and bamboo, as raw materials to produce various automobile parts, which can also be cost-effective and better than the existing material. 4 REFERENCES [1] Robson, J., Hague, J., Newman, G., Jeronimidis, G., Ansell. MP. (1993). Survey of natural materials for use in structural composites as reinforcement and matrices. Report to DTI LINK Structural Composites Committee. Report No. EC/431/92, University of Wales, Cardiff. [2] Monteiro, S.N., Calado, V., Rodriguez, R.J.S., Margem, F.M. (2012). 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Vsebina Strojniški vestnik - Journal of Mechanical Engineering letnik 67, (2021), številka 10 Ljubljana, oktober 2021 ISSN 0039-2480 Izhaja mesecno Razširjeni povzetki (extended abstracts) Maciej Kowalski, Przemyslaw Zawadzki, Adam Hamrol: Uspešnost samodejnega programiranja CAM s predlogami za strojno obdelavo v proizvodnji specialnih produkcijskih orodij SI 61 Shuai Yang, Xing Luo, Chuan Li: Diagnosticiranje napak na reduktorjih RV za industrijske robote na osnovi konvolucijske nevronske mreže SI 62 Zisheng Wang, Bo Li, Chao Liang, Xuewen Wang, Jiahao Li: Analiza odziva strgalnega transporterja na odpoved verige na osnovi MBD-DEM-FEM SI 63 Yong Chen, Jinjin Tan, Guoping Xiao: Preiskava globine prijemanja zob zagozdnega obešala v zašcitne cevi in mehanskih lastnosti zašcitnih cevi pri razlicnih obesnih obremenitvah v zelo globokih vrtinah SI 64 Saravanan Kandasamy Ganesan, Thanigaivelan Rajasekaran: Optimizacija parametrov laserske obdelave in geometrije jamic po kombinirani metodi PCA-GRG SI 65 Rajmohan Bose, Arunachalam Kandavel: Karakterizacija mehanskih lastnosti in strukturni atributi biohibridnih kompozitov na osnovi vlaken iz konoplje, bambusa in jute: alternativni pristop k uporabi naravnih vlaken za avtomobilske dele SI 66 Strojniški vestnik - Journal of Mechanical Engineering 67(2021)10, SI 61 Prejeto v recenzijo: 2021-05-28 Š 2021 Strojniški vestnik. Vse pravice pridržane. Prejeto popravljeno: 2021-08-17 Odobreno za objavo: 2021-08-18 Uspešnost samodejnega programiranja CAM s predlogami za strojno obdelavo v proizvodnji specialnih produkcijskih orodij Maciej Kowalski1,2 – Przemyslaw Zawadzki1,* – Adam Hamrol1 1 Tehniška univerza v Poznanu, Fakulteta za strojništvo, Poljska 2 MK-Tech, Poljska Proizvodnja specialnih tehnoloških orodij je primer proizvodnje po narocilu (ang. ETO ~ engineering to order), kjer konstrukcija orodja bazira na kupcevem modelu izdelka. Proces je zamuden in pogosto tudi precej neucinkovit (pri proizvodnji skupin izdelkov), saj ga je treba izvesti v celoti za vsak model izdelka posebej, ceprav si izdelki delijo podobne proizvodne operacije. Avtorji so že razvili poseben postopek ACPUT (samodejno programiranje CAM s predlogami za obdelavo), ki zapolnjuje nekaj vrzeli v programiranju CAM in omogoca pohitritev tega procesa (vsaj v teoriji). Glavni cilj pricujoce raziskave je bil preucitev uspešnosti in ucinkovitosti postopka ACPUT v primerjavi s tradicionalnim programiranjem CNC-strojev v industrijski praksi. Uvedba metod za samodejno programiranje CAM kot je ACPUT v oddelkih za tehnologijo proizvodnih podjetij je naceloma možna, zahteva pa napredno znanje in vešcine na podrocju CAM ter dostop do implicitnega znanja v podjetju. Priprava takih rešitev je težavna in casovno zamudna, zato je bilo treba preveriti, kdaj je avtomatizacija programiranja CAM sploh smiselna. Opravljen je bil test za primerjavo ucinkovitosti ACPUT in tradicionalnega programiranja. Sodelovalo je 10 programerjev CAM, od tega 5 strokovnjakov z vec leti delovnih izkušenj in 5 zacetnikov z le nekaj meseci izkušenj. V analizi so bili primerjani naslednji kazalniki: 1. potrebni cas za pripravo programa za obdelavo posameznega dela orodja Ti [min], 2. kumulativni cas za obdelavo, ki je potreben za pripravo skupine orodij Tc [min], 3. vsota stroškov programiranja in obdelave vsakega orodja Cm [EUR]. Za preizkuse je bilo izbrano orodje za montažo plasticnih cevi, ki je sestavljeno iz 50 delov in se izdeluje na CNC-rezkalnem stroju. Preizkusi v industrijskih pogojih so pokazali, da lahko postopek ACPUT skrajša potrebni cas za razvoj programov za obdelavo, kar pozitivno vpliva na skupne stroške izdelave orodij. Uspešnost postopka ACPUTje bila preizkušena na osnovi analize potrebnega casa za pripravo CNC programov in samega casa izvajanja obdelovalnih operacij (na osnovi simulacij v paketu CAM). Cas priprave programa je neposredni (poleg kakovostnih zahtev) kazalec uspešnosti postopka ACPUT. Analiza rezultatov je pokazala, da samodejna rešitev prinaša signifikantne casovne prednosti, zlasti v primerjavi z manj izkušenimi tehnologi. Rezultati samodejne rešitve so bili celo boljši od rešitev izkušenih strokovnjakov. Poudariti pa je treba, da je uspešnost postopka ACPUT odvisna predvsem od izkušenj ljudi, ki pripravijo predloge. V prihodnjih raziskavah bi bilo smiselno preveriti možnost ustvarjanja programov za specificne tipe CNC krmilnikov. Do tedaj pa bodo bolj neposredne raziskave osredotocene na razvoj metod za hitro oceno tehnološke podobnosti delov, ki so kandidati za pripravo predlog za obdelavo. Ce so razlike med deli prevelike, priprava predlog in uporaba postopka ACPUT nista ekonomsko upraviceni – v tem primeru ima prednost tradicionalni postopek programiranja. ACPUT je nova metoda programiranja v sistemih CAM, ki je osredotocena na avtomatizacijo ponavljajocih se opravil, zlasti v proizvodnji orodij. Metoda temelji na namensko ustvarjeni bazi znanja in na predlogah za obdelavo, ustvarjenih v sistemu CAM. Rezultati bodo zaradi prakticne uporabnosti v industrijski proizvodnji uporabni tako za industrijske in procesne inženirje, kakor tudi za raziskovalce v proizvodnih podjetjih. Kljucne besede: generativni proces CAM, CNC programiranje, avtomatizacija tehnološke priprave, na znanju temeljeci inženiring Strojniški vestnik - Journal of Mechanical Engineering 67(2021)10, SI 62 Prejeto v recenzijo: 2021-06-07 Š 2021 Strojniški vestnik. Vse pravice pridržane. Prejeto popravljeno: 2021-08-17 Odobreno za objavo: 2021-09-09 Diagnosticiranje napak na reduktorjih RV za industrijske robote na osnovi konvolucijske nevronske mreže Shuai Yang1,* – Xing Luo2 – Chuan Li 2 1 Nacionalno raziskovalno središce za pametne proizvodne storitve, Tehniška in poslovna univerza v Chongqingu, Kitajska 2 Šola za menedžment in inženiring, Tehniška in poslovna univerza v Chongqingu, Kitajska Odpoved reduktorja kot kljucne komponente mehanskega pogonskega sistema lahko povzroci veliko gospodarsko škodo, v izjemnih primerih pa tudi resne poškodbe ali smrt. V clanku je opisana rešitev tega problema. Veksperimentih je bil uporabljen senzor pospeška za zajem podatkov o obratovanju reduktorja v normalnem stanju in v stanju napake. Zbrani podatki so bili pretvorjeni iz casovne v frekvencno domeno po metodi 2D-FFT, nato pa so bile z 2D konvolucijsko nevronsko mrežo (2D-CNN) samodejno pridobljene znacilke signalov v frekvencni domeni in opravljena je bila njihova klasifikacija s funkcijo Softmax. Opravljene so bile tudi eksperimentalne primerjave z drugimi prevladujocimi metodamiza dodatno validacijo tocnosti klasifikacije napak. Predmet clanka je reduktor z rotacijskim vektorjem (RV) za industrijske robote. Signali vibracij so bili pretvorjeni iz casovne v frekvencno domeno po metodi 2D-FFT. Nevronska mreža 2D-CNN je bila uporabljena za samodejno pridobivanje znacilk signala v frekvencni domeni in klasifikacijo podatkov o napakah skladno z lastnostmi razlicnih signalov. Nato je bil uporabljen algoritem vzvratnega postopka ucenja za posodobitev parametrov modela v 2D-CNN. Predlagana metoda je bila uporabljena na reduktorju RV za industrijske robote, in sicer s podatki za en primer normalnega obratovanja in pet primerov stanj napak. Celotna tocnost diagnosticiranja stanj je dosegla 99,67 %. Stopnja prepoznavanja napak št. 1, 3 in 4 je bila 100 %. Tocnost prepoznavanja normalnega stanja je bila 99 %, tocnost prepoznavanja napake št. 2 je znašala 99,3 %, napake št. 5 pa 99,67 %. Za primerjavo so bili uporabljeni modeli 1D-CNN, DBN, NN in SAE, katerih stopnja tocnosti znaša 98,46 %, 58,08 %, 83,47 % in 83,05 %. Rezultati eksperimentov dokazujejo, da ima predlagani model v primerjavi z ostalimi modeli dobro tocnost diagnosticiranja napak. Predlagana metoda je bila uporabljena samo pri reduktorjih RV za industrijske robote, v prihodnje pa bodo raziskave za preverjanje univerzalnosti metode razširjene še na menjalnike za drugo mehansko opremo. Dodan bo tudi primerjalni model. Model CNN za zaznavanje in klasifikacijo napak dosega obetavne rezultate na razlicnih podrocjih. Uporaba modela CNN pri reduktorjih RVza industrijske robote je danes še redka. Vclanku sta bili povezani metodi 2D-FFT in 2D-CNN za diagnosticiranje napak, predlagana metoda pa je bila uporabljena na reduktorju RVza industrijske robote. Kljucne besede:diagnosticiranje napak, konvolucijska nevronska mreža, reduktor RV Strojniški vestnik - Journal of Mechanical Engineering 67(2021)10, SI 63 Prejeto v recenzijo: 2021-06-24 Š 2021 Strojniški vestnik. Vse pravice pridržane. Prejeto popravljeno: 2021-09-05 Odobreno za objavo: 2021-09-21 Analiza odziva strgalnega transporterja na odpoved verige na osnovi MBD-DEM-FEM Zisheng Wang1,2 – Bo Li1,2 – Chao Liang1,2 – Xuewen Wang1,2,* – Jiahao Li1,2 1 Tehniška univerza v Taiyuanu, Kolidž za strojništvo in avtomobilsko tehniko, Kitajska 2 Državni laboratorij za mehanizirano opremo za premogovnike v provinci Shanxi, Kitajska Verige strgalnih transporterjev se pogosto kvarijo, te okvare pa je težko preucevati eksperimentalno. Vecina simulacij odpovedi v objavljenih raziskavah je bila opravljena po metodah, ki le vprašljivo simulirajo resnicna delovna stanja strgalnega transporterja. V clanku je opisana analiza odgovora na odpoved po metodi sklopitve MBD-DEM-FEM (dinamika vec teles/metoda diskretnih elementov/metoda koncnih elementov). Oblikovan je bil sklopljen model strgalnega transporterja MBD-DEM-FEM, ki je bil verificiran eksperimentalno in po tockovni metodi. Dolocen je bil odgovor verige in razsutega premoga v pogojih okvare in rezultati so bili delno verificirani. Koncno so bile analizirane odvisnosti med faktorjem intenzitete napetosti in parametri zloma po metodi odzivnih površin. Na podlagi testa transporta je bila uporabljena metoda odzivnih površin za optimizacijo kontaktnih parametrov premoga s silo in kotom zlaganja kot odzivnima spremenljivkama. Model DEM-MBD je bil verificiran po tockovni metodi, model verige po FEM (MKE) pa z nateznim preizkusom. Sklopljeni model je bil nato razrešen za dolocitev odgovora verige in premoga, rezultati pa so bili delno verificirani. Koncno je bila uporabljena še metoda odzivnih površin za analizo odvisnosti med faktorjem intenzivnosti napetosti in parametri zloma. Rezultati, ugotovitve: (1) Sklopljeni model MBD-DEM-FEM je bil verificiran eksperimentalno in teoreticno, relativna napaka pa je sprejemljiva. (2) Ko veriga obtici in se polomi, povzroci odklon transporterja neenakomerno obremenitev verig. Napetost in vibracije napete verige se hitro povecajo, medtem ko se napetost,hitrost in vibracije ohlapne verige zmanjšajo. Obremenitev verige ob okvari povzroci plasticno deformacijo verige in zmanjša njeno utrujenostno trajnostno dobo. Obraba srednje plošce je koncentrirana na poti verige, okvare pa povzrocijo novo obrabo na površini srednje plošce. (3) Zacetni kot z drugimi faktorji negativno vpliva na hitrost sprememb K1max, medtem ko imata globina razpok in natezna obremenitev pozitiven vpliv nanjo. Omejitve raziskave, implikacije: (1) Robovi in vogali kosov premoga niso bili jasno izoblikovani zaradi krajšega casa izvajanja simulacije. Z razvojem vzporednega racunanja bo mogoce uporabiti modele kosov premoga z izoblikovanimi robovi in vogali za tocnejše rezultate. (2) Premog je krhek, toda drobljenje zaradi krajšega casa izvajanja simulacije ni bilo upoštevano. V prihodnjih raziskavah bo za združevanje kosov premoga mogoce uporabiti model spajanja. (3) Veriga je upogljiva. Vclanku je bila izracunana le deformacija verige po izlocitvi obremenitve zaradi okvare. Vprihodnje bo mogoce uporabiti dinamicni model upogljive verige za dolocitev vpliva deformacije verige na transport v realnem casu. Prispevek, novosti, vrednost: (1) Kontaktni parametri za premog so bili umerjeni s silo in kotom zlaganja kot odzivnima spremenljivkama. (2) S sklopljeno metodo MBD-DEM-FEM je bil simuliran realen prehodni odgovor verige in razsutega premoga v primeru, da veriga obtici in se polomi. Napovedan je bil tudi vpliv okvare na obrabo in na utrujenostno trajnostno dobo verige. (3) Odvisnosti med velikostjo napetosti in parametri zloma so bile analizirane po metodi odzivnih površin. Pricujoci clanek bo lahko uporaben za simulacije strgalnih transporterjev in mehanizmov okvar ter za optimizacijo konstrukcije verig. Kljucne besede: dinamika vec teles, metoda diskretnih elementov, metoda koncnih elementov, okvara verige strgalnega transporterja, faktor intenzivnosti napetosti, metoda odzivnih površin Preiskava globine prijemanja zob zagozdnega obešala v zašcitne cevi in mehanskih lastnosti zašcitnih cevi pri razlicnih obesnih obremenitvah v zelo globokih vrtinah Yong Chen – Jinjin Tan – Guoping Xiao Jugozahodna univerza za nafto, Šola za strojništvo in elektrotehniko, Kitajska Namestitveno obremenitev velikih zagozdnih obešal za zašcitne cevi, ki se uporabljajo na naftnih poljih na severozahodu Kitajske, je težko nadzorovati in zato jo je nujno treba pravilno izbrati. Z dolocitvijo mejne namestitvene obremenitve in z ustreznimi mehanskimi lastnostmi glave vrtine z velikimi zašcitnimi cevmi je mogoce prepreciti porušitev zašcitnih cevi. Clanek obravnava odvisnost med globino prijemanja zob zagozdnega obešala in obesno obremenitvijo obešala tipa WA velikosti .273 mm, kot se uporablja na naftnih poljih na severozahodu Kitajske. Odvisnost je bila dolocena eksperimentalno, s teoreticnimi izracuni in z analizo po metodi koncnih elementov. V clanku je bila najprej uporabljena metoda opazovanja oz. opis operativnih težav, ki se pojavljajo na omenjenih naftnih poljih. Nadzorovanje namestitvenih obremenitev postaja vse težje zaradi velikih zašcitnih cevi, ki se vlagajo v vrtine pri globokih nahajališcih nafte. Cezmerna namestitvena obremenitev povzroca težave zaradi pogrezanja glav vrtin in resnih poškodb zašcitnih cevi, ki jih povzrocijo zagozde, nezadostna namestitvena obremenitev pa lahko povzroci pomanjkljivo tesnjenje glav zašcitnih cevi. V nadaljevanju je bila uporabljena raziskovalna metoda za potrjevanje dejstev in oblikovanje raziskovalne teme. Interakcije med zagozdami in zašcitno cevjo ter namestitev obešal za liner povzrocijo spremembo porazdelitve napetosti v zašcitni cevi. V zadnjih dveh letih je bilo izvrtanih deset velikih vrtin na globokih nahajališcih in težave so nastopile pri inštalaciji treh glav vrtin. Clanek koncno podaja še primerjalno analizo. Primerjava je pokazala dobro ujemanje rezultatov eksperimentov z rezultati numericnih simulacij po metodi koncnih elementov, s tem pa je bila dokazana kredibilnost raziskave. Študija je pokazala, da so sledovi oprijema vložne zagozde na zašcitni cevi globlji v spodnjem delu. Ko se obesna obremenitev poveca s 1000 kN na 6000 kN, najvecja globina prijema vložnih zagozd v zašcitni cevi postopoma zrase z 0,07163 mm na 0,3947 mm. Povecanje globine prijema zagozd je vecje v nižjih legah (manjše število). Ko se preostala porušitvena trdnost zmanjša na doloceno vrednost, je mogoce dolociti najvecjo obešalno silo, ki ustreza globini penetracije vložne zagozde. Na osnovi analize po metodi koncnih elementov in izpeljave teoreticne enacbe je bila dolocena porazdelitev napetosti v steni zašcitne cevi z najglobljo sledjo vložne zagozde. Sila, s katero zobje zagozde delujejo na zašcitno cev, je težko dolocljiva in se spreminja pri razlicnih obesnih obremenitvah. To bo tudi predmet prihodnjih raziskav. Vecina objavljenih clankov obravnava predvsem zagozde, prakticno nihce pa ni raziskoval delovanja sil na zašcitne cevi. V tej študiji je bila z analizo po metodi koncnih elementov in s teoreticnimi izracuni opredeljena odvisnost med globino prijema vložne zagozde in obesno obremenitvijo zagozdnega obešala tipa WA, kot se uporablja na naftnih poljih na severozahodu Kitajske. Dolocena je bila mejna namestitvena obremenitev za glave velikih vrtin, ki preprecuje porušitev zašcitnih cevi. Izpeljana je bila formula za racunanje porazdelitve notranjih napetosti v rizicnem predelu zašcitne cevi, ki bo lahko v pomoc pri projektiranju vrtin in pri obratovanju naftnih polj. Kljucne besede: zagozdno obešalo tipa WE, zašcitna cev, analiza po metodi koncnih elementov, globina prijemanja zagozdnega obešala, preostala porušitvena trdnost, obesna obremenitev Strojniški vestnik - Journal of Mechanical Engineering 67(2021)10, SI 65 Prejeto v recenzijo: 2021-05-06 Š 2021 Strojniški vestnik. Vse pravice pridržane. Prejeto popravljeno: 2021-07-27 Odobreno za objavo: 2021-09-24 Optimizacija parametrov laserske obdelave in geometrije jamic po kombinirani metodi PCA-GRG Saravanan Kandasamy Ganesan1 – Thanigaivelan Rajasekaran2,* 1 Tehniški kolidž Sona, Indija 2 Tehniški kolidž Muthayammal, Indija Raziskovalci modificirajo površine zlitin za razlicne aplikacije s pomocjo laserjev. Za optimalen proces laserske obdelave, ki zagotavljaucinkovito in visokonatancno izdelavo mikrojamic, je nujen ustrezen nadzor nad vhodnimi parametri. Ceprav se laserji uporabljajo za modifikacijo površine zlitin, pa ni sistematicnih študij na temo vecciljne optimizacije laserskega procesa. Mikrojamice so bile izdelane na površini jekla SS316L, vplivi dejavnikov laserskega procesa, kot so frekvenca (F), povprecna moc (P) in trajanje impulza (t) na povprecni premer jamic (f), razdaljo jamic (d) in globino jamic (l), pa so bili preuceni z ortogonalnim poljem (OA) L9. Optimizacija odgovorov na izhodu laserskega procesa je bila opravljena z analizo variance (ANOVA) in s tehnikami vecciljne optimizacije, vkljucno s sivo relacijsko analizo (GRA) in analizo glavnih komponent (PCA). Parametri laserske obdelave morajo biti optimizirani za dano geometrijo, saj lahko sicer negativno vplivajo na izhodne performanse. Za material obdelovanca je bila izbrana zlitina SS316L. Uporabljenih je bilo devet preizkušancev dolžine 25 mm, širine 25 mm in debeline 5 mm. V eksperimentih so bili zajeti naslednji faktorji: frekvenca (F) v kHz, povprecna moc (P) v vatih in trajanje impulza (t) v nanosekundah. Vrednosti faktorjev so bile izbrane na podlagi preliminarnih eksperimentov. Za vsakega od faktorjev so bile izbrane po tri stopnje, kot je opisano v preglednici 1. Ortogonalno polje je bilo izbrano glede na prostostne stopnje (DoF). Povprecni premer jamice (f), razdalja jamic (d) in globina (l) so bili izmerjeni po trikrat. Mikrojamice so bile uspešno izdelane na površini preizkušancev iz zlitine SS316L. Optimalne nastavitve laserske obdelave mikrojamic, primernih za tribološke aplikacije, so: frekvenca 15 kHz, povprecna moc 12 W in trajanje impulza 1500 ns. Na nastanek mikrojamic imata najvecji vpliv frekvenca in povprecna moc. Nadzorovani faktorji, ki vplivajo na razlicne karakteristike delovanja laserskega procesa, si po signifikantnosti sledijo takole: frekvenca, trajanje impulza in povprecna moc. S povecanjem moci laserja se zmanjšajo izhodne performanse. V prihodnjih študijah bo razvit raziskovalni model za napovedovanje števila mikrojamic na danem obmocju na osnovi RSM. Na površini jekla SS316L so bile izdelane mikrojamice, vplivi dejavnikov laserskega procesa, kot so frekvenca, povprecna moc in trajanje impulza na povprecni premer jamic, razdaljo jamic in globino jamic, pa so bili preuceni z ortogonalnim poljem L9. Performancni kriteriji so bili optimizirani s kombinirano metodo PCA­GRG. Kljucne besede: laser, mikrojamica, analiza glavnih komponent, siva relacijska stopnja, nerjavno jeklo Karakterizacija mehanskih lastnosti in strukturni atributi biohibridnih kompozitov na osnovi vlaken iz konoplje, bambusa in jute: alternativni pristop k uporabi naravnih vlaken za avtomobilske dele Rajmohan Bose* – Arunachalam Kandavel Univerza Anna, Kampus MIT, Indija Vpricujoci raziskavi so bile tri vrste naravnih vlaken iz jute, konoplje in bambusa skupaj s prahom školjcnih lupin in s polipropilensko smolo hibridizirane v biohibridne kompozite. Obravnavanih je bilo devet preizkušancev z razlicnimi masnimi deleži bambusa, konoplje in jute. Prešanje je nacin predelave umetnih mas, pri katerem se vnaprej ogret material dozira v gnezdo odprtega in ogrevanega orodja. Omenjeni kompoziti so bili izdelani s prešanjem naravnih vlaken in polipropilena v razmerju 1 : 4. Kompoziti sestojijo iz 100 gramov vlaken, 400 gramov smole PPin 5 gramov školjcnih lupin v prahu. Opravljena je bila primerjava mehanskih lastnosti devetih preizkušancev, vkljucno z upogibno, udarno in natezno trdnostjo. Rezultati so zelo dobri, zasluga za to pa gre predvsem prisotnosti jute in vzorcu polaganja. Preizkušanec št. 9 je namrec poleg 20 % konoplje in 20 % bambusa vseboval tudi 60 % jute, kar je vec kot pri ostalih osmih preizkušancih. Natezna trdnost kompozitnega preizkušanca št. 9 je bila za 64,1 % (29,74 MPa) višja kot pri obstojecem materialu (18,1 MPa) in je znašala 47,84 MPa. Upogibna trdnost preizkušanca št. 9 je znašala 239,36 MPa, oz. za 150,96 % vec kot pri obstojecem materialu (68,2 MPa). Udarna energija preizkušanca št. 9 je bila petkrat vecja (18,33 J) kot pri obstojecem materialu (3,6 J). To pomeni, da lahko material vzdrži nenadne udarce brez deformacij, kar je kljucna lastnost za avtomobilske dele. Na preizkušancu št. 9 so bile opravljene tudi morfološke analize, termogravimetricna analiza in analiza po metodi koncnih elementov. Z morfološko analizo je bilo raziskano oblikovanje struktur in spajanje materialov na vmesnih ploskvah. Rezultati SEM kažejo, da na zgornji površini in v notranjih slojih ni votlin, zracnih mehurckov ali razpok. Zmes smole in vlaken je bila enakomerna pri vseh biohibridnih kompozitih. Preizkušanec št. 9 je bil termogravimetricno analiziran skupaj z obstojecim materialom za dolocitev masnih izgub pri povišanih temperaturah. Obstojeci material je imel povprecno temperaturo 536,3 °C, preizkušanec št. 9 pa je imel povprecno temperaturo 430,6 °C, kar je za 19,4 % manj kot pri obstojecem materialu. Zmanjšanje mase je mogoce pojasniti s sestavo kompozita, ki vsebuje vlakna naravnega izvora. Delovna temperatura notranjih in zunanjih delov avtomobilov je nižja od 460,6 °C in material je zato primeren za avtomobilske dele. Primerjava rezultatov eksperimentov z rezultati analize po metodi koncnih elementov (MKE) je pokazala dobro ujemanje in odsotnost vecjih odstopanj. Kompoziti z vlakni naravnega izvora, kot so vlakna iz jute, konoplje in bambusa, so glede na rezultate primerni za proizvodnjo razlicnih avtomobilskih delov. Ti materiali so lahko cenovno ugodni in boljši od obstojecega materiala. Kljucne besede: termogravimetricna analiza, morfološka analiza, juta, konoplja, bambus, analiza po metodi koncnih elementov Guide for Authors All manuscripts must be in English. Pages should be numbered sequentially. The manuscript should be composed in accordance with the Article Template given above. The maximum length of contributions is 12 pages (approx. 5000 words). Longer contributions will only be accepted if authors provide justification in a cover letter. For full instructions see the Infor­mation for Authors section on the journal’s website: http://en.sv-jme.eu . 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Please add DOI code when available. Examples follow. Journal Papers: Surname 1, Initials, Surname 2, Initials (year). Title. Journal, volume, number, pages, DOI code. [1] Hackenschmidt, R., Alber-Laukant, B., Rieg, F. (2010). Simulating nonlinear materials under centrifugal forces by using intelligent cross-linked simulations. Strojniški vest-nik - Journal of Mechanical Engineering, vol. 57, no. 7-8, p. 531-538, DOI:10.5545/sv­jme.2011.013. Journal titles should not be abbreviated. Note that journal title is set in italics. Books: Surname 1, Initials, Surname 2, Initials (year). Title. Publisher, place of publication. [2] Groover, M.P. (2007). Fundamentals of Modern Manufacturing. John Wiley & Sons, Hoboken. Note that the title of the book is italicized. Chapters in Books: Surname 1, Initials, Surname 2, Initials (year). Chapter title. Editor(s) of book, book title. Publisher, place of publication, pages. [3] Carbone, G., Ceccarelli, M. (2005). Legged robotic systems. Kordi˜, V., Lazinica, A., Merdan, M. (Eds.), Cutting Edge Robotics. Pro literatur Verlag, Mammendorf, p. 553­576. Proceedings Papers: Surname 1, Initials, Surname 2, Initials (year). Paper title. Proceedings title, pages. [4] Štefani˜, N., Martin°evi˜-Miki˜, S., Tošanovi˜, N. (2009). Applied lean system in process industry. MOTSP Conference Proceedings, p. 422-427. Standards: Standard-Code (year). Title. Organisation. Place. [5] ISO/DIS 16000-6.2:2002. Indoor Air – Part 6: Determination of Volatile Organic Com­pounds in Indoor and Chamber Air by Active Sampling on TENAX TA Sorbent, Thermal Desorption and Gas Chromatography using MSD/FID. International Organization for Standardization. Geneva. WWW pages: Surname, Initials or Company name. Title, from http://address, date of access. [6] Rockwell Automation. Arena, from http://www.arenasimulation.com, accessed on 2009­09-07. EXTENDED ABSTRACT: When the paper is accepted for publishing, the authors will be requested to send an ex­tended abstract (approx. one A4 page or 3500 to 4000 characters or approx. 600 words). The instruction for composing the extended abstract are published on-line: http://www.sv-jme. eu/information-for-authors/ . COPYRIGHT: Authors submitting a manuscript do so on the understanding that the work has not been published before, is not being considered for publication elsewhere and has been read and approved by all authors. The submission of the manuscript by the authors means that the authors automatically agree to transfer copyright to SV-JME when the manuscript is accepted for publication. All accepted manuscripts must be accompanied by a Copyright Transfer Agreement, which should be sent to the editor. The work should be original work by the authors and not be published elsewhere in any language without the written consent of the publisher. The proof will be sent to the author showing the final layout of the article. Proof correction must be minimal and executed quickly. Thus it is essential that manuscripts are accurate when submitted. Authors can track the status of their accepted articles on http://en.sv-jme.eu/. PUBLICATION FEE: Authors will be asked to pay a publication fee for each article prior to the article appear­ing in the journal. However, this fee only needs to be paid after the article has been accepted for publishing. The fee is 380 EUR (for articles with maximum of 6 pages), 470 EUR (for ar­ticles with maximum of 10 pages), plus 50 EUR for each additional page. The additional cost for a color page is 90.00 EUR (only for a journal hard copy; optional upon author’s request). These fees do not include tax. followed closely. Symbols for physical quantities in the text should be written in italics (e.g. v, T, n, etc.). Symbols for units that consist of letters should be in plain text (e.g. ms-1, K, min, Strojniški vestnik -Journal of Mechanical Engineering mm, etc.). Please also see: http://physics.nist.gov/cuu/pdf/sp811.pdf . Ašker°eva 6, 1000 Ljubljana, Slovenia, e-mail: info@sv-jme.eu http://www.sv-jme.eu Contents Papers 401 Maciej Kowalski, Przemyslaw Zawadzki, Adam Hamrol: Effectiveness of Automatic CAM Programming Using Machining Templates for the Manufacture of Special Production Tooling 489 Shuai Yang, Xing Luo, Chuan Li: Fault Diagnosis of Rotation Vector Reducer for Industrial Robot Based on a Convolutional Neural Network 501 Zisheng Wang, Bo Li, Chao Liang, Xuewen Wang, Jiahao Li: Response Analysis of a Scraper Conveyor under Chain Faults Based on MBD-DEM-FEM 516 Yong Chen, Jinjin Tan, Guoping Xiao: Investigation on the Depth of Slip Hanger Teeth Bite into Casing and the Mechanical Properties of Casing under Different Suspension Loads in Ultra-Deep Wells 525 Saravanan Kandasamy Ganesan, Thanigaivelan Rajasekaran: Optimization of Laser Parameters and Dimple Geometry Using PCA-Coupled GRG 534 Rajmohan Bose, Arunachalam Kandavel: Mechanical Characterization and Structural Attributes of Biohybrid Composites Derived Using Hemp, Bamboo, and Jute Fibres: an Alternative Approach in the Application of Natural Fibres in Automobile Parts