Optimization of micro-EDM parameters using grey-based fuzzy logic coupled with the Taguchi method
Optimizacija parametrov mikroelektroerozije z uporabo mehke logike v povezavi s Taguchi metodo
M. S. Vijayanand, M. Ilangkumaran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 989
Mechanism of multi-layer composite coatings in the zinc process of recycling coated WC-Co cemented-carbide scrap
Mehanizem ve~plastnih kompozitnih premazov v procesu cinkanja za recikliranje odpadkov opla{~enih WC-Co karbidnih trdin
H. Kuang, D. Tan, W. He, X. Wang, J. Zhong, H. Wang, C. Yang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 997
Basic physical, mechanical and electrical properties of electrically enhanced alkali-activated aluminosilicates
Osnovne fizikalne, mehanske in elektri~ne lastnosti elektri~no izbolj{anih, z alkalijami aktiviranih aluminosilikatov
L. Fiala, M. Jerman, P. Rovnaník, R. ^erný . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1005
Modeling of water removal in direct-chill casting of aluminum-alloy billets
Modeliranje omejevanja neposrednega hlajenja z vodo med vertikalnim konti litjem gredic iz Al-zlitin
A. Meysami, S. Mahmoudi, M. Hajisafari . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1011
Improving the microstructure and mechanical properties of magnesium-alloy sheets with a new extrusion method
Izbolj{anje mikrostrukture in mehanskih lastnosti plo~evine iz Mg zlitine z novo metodo iztiskanja
L. Lu, Z. Yin, Y. Liu, D. Chen, C. Liu, Z. Wu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1019
Formation mechanism of diffusion-reaction layer for a Cu/Ti diffusion couple under different heating methods
Oblikovanje mehanizma difuzijsko reakcijske plasti na Cu/Ti povr{ini z razli~nimi metodami segrevanja
L. Fei, W. Mingfang, P. Juan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1025
De-oxidation of PK942 steel with Ti and Zr
Dezoksidacija jekla PK942 s Ti in Zr
M. Kole`nik, J. Burja, B. [etina Bati~, A. Nagode, J. Medved . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1031
Corrosion on polished and laser-textured surfaces of an Fe–Mn biodegradable alloy
Primerjava korozijskih lastnosti polirane in lasersko teksturirane povr{ine biorazgradljive zlitine Fe–Mn
M. Ho~evar, ^. Donik, I. Paulin, A. Kocijan, F. Tehovnik, J. Burja, P. Gregor~i~, M. Godec. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1037
Comparison of the surface and anticorrosion properties of SiO2 and TiO2 nanoparticle epoxy coatings
Primerjava povr{inskih in protikorozijskih lastnosti epoksidnih prevlek obogatenih s SiO2 in TiO2 nanovklju~ki
M. Conradi, A. Kocijan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1043
LETNO KAZALO – INDEX
Letnik 51 (2017), 1–6 – Volume 51 (2017), 1–6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1047
2 Materiali in tehnologije / Materials and technology 51 (2017) 6,
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967–2017) – 50 LET/50 YEARS
M. KOVA^I^ et al.: INCREASING THE TENSILE STRENGTH AND ELONGATION OF 16MnCrS5 STEEL ...
883–888
INCREASING THE TENSILE STRENGTH AND ELONGATION OF
16MnCrS5 STEEL USING GENETIC PROGRAMMING
POVE^EVANJE NAPETOSTNE TRDNOSTI IN RAZTEZKA
16MnCrS5 JEKLA Z UPORABO GENETSKEGA PROGRAMIRANJA
Miha Kova~i~1,2, Ana Turn{ek1, Darja Ocvirk1, Ga{per Gantar3
1[tore Steel d.o.o., @elezarska cesta 3, 3220 [tore, Slovenia
2Institute of Metals and Technology, Lepi pot 11, 1000 Ljubljana, Slovenia
3College of industrial Engineering, Mariborska cesta 2, 3000 Celje, Slovenia
miha.kovacic@store-steel.si
Prejem rokopisa – received: 2016-09-29; sprejem za objavo – accepted for publication: 2017-05-05
doi:10.17222/mit.2016.293
[tore Steel Ltd. is one of the largest spring-steel producers in Europe. [tore Steel makes more than 1400 steel grades of
different chemical composition. Among them is 16MnCrS5 steel. It is generally used for the fabrication of case-hardened
machine parts for various applications (e.g., bars, rods, plates, strips, forgings), where a combination of wear resistance,
toughness and dynamic strength is essential. These properties can be easily correlated with tensile strength, which depends on
the chemical composition and heat treatment after rolling. In addition, the elongation should be taken into account. In the paper,
modeling of tensile strength and elongation with genetic programming is presented and compared with linear regression
modeling. The chemical composition data (content of C, Mn, S and Cr) and the heat-treatment regime data (GKZ or BG
annealing) were used for modeling. The modeling results show that a higher tensile strength with improved elongation were
achieved.
Keywords: 16MnCrS5, tensile strength, elongation, modeling, genetic programming, linear regression
[tore Steel je najve~ji proizvajalec vzmetnega jekla v Evropi. [tore Steel izdeluje ve~ kot 1400 razli~nih kvalitet jekla z
razli~nimi kemijskimi sestavami. Med njmi tudi 16MnCrS5, ki spada v skupino jekel za cementacijo, ki so namenjena za strojno
obdelavo razli~nih delov (npr. palic, plo{~, trakov, odkovkov), kjer se zahteva kombinacija obrabne odpornosti, `ilavosti ter
trajnonihajne trdnosti. Le-te lastnosti lahko povezujemo z natezno trdnostjo, ki je odvisna predvsem od kemi~ne sestave in
toplotne obdelave po valjanju. Prav tako je pomemben raztezek. V ~lanku je predstavljeno modeliranje natezne trdnosti in
raztezka s pomo~jo genetskega modeliranja in linearne regresije. Za modeliranje smo uporabili vsebnosti kemijskih elementov
(C, Mn, S in Cr) ter na~in toplotne obdelave (GKZ ali BG). Glede na rezultate smo pove~ali natezno trdnost pri izbolj{anem
raztezku.
Klju~ne besede: 16MnCrS5, natezna trdnost, raztezek, modeliranje, genetsko programiranje, linearna regresija
1 INTRODUCTION
In the modern steel-production and steel-consump-
tion industry, it is essential to know the material pro-
perties and behavior. There are several well-known
commercial types of software available for modeling
material properties but steel producers are often forced
on using inventive experiments, methods and approaches
due to their unique production, equipment, time con-
straints and niche applications.1–6
The literature review reveals that tensile strength and
elongation optimization of steel products in general
incorporates multi-criteria optimization approaches,1,2,7–9
based also on artificial intelligence methods.10–14This is
the case for rated material properties in both quantitative
and qualitative terms.1,10
The required tensile strength and elongation are ob-
tained by changing:
• chemical composition,2,12,14
• plastic deformation parameters (i.e., influencing mi-
crostructure, grain size),8,15–17
• heat-treatment parameters after plastic deforma-
tion.2,7,14
The article presents the practical implementation of
tensile strength and elongation optimization for long-
rolled products made of 16MnCrS5 steel, which is
generally used for the fabrication of case-hardened
machine parts for several applications (e.g., bars, rods,
plates, strips, forgings).
First, we provide the experimental background, then
the methods used, and, lastly, we present the practical
implementation and draw conclusions.
2 EXPERIMENTAL BACKGROUND
In general, production starts with scrap melting in an
electro arc furnace (EAF).18 After the scrap and car-
burizing agents have been melted, dephosphorization is
conducted. The melting bath is heated up to the tapping
temperature and, after secondary steel treatment, is
discharged into the casting ladle.
After discharging from EAF, the melting bath is
deoxidated, desulphurized, the nonmetallic inclusions
Materiali in tehnologije / Materials and technology 51 (2017) 6, 883–888 883
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967–2017) – 50 LET/50 YEARS
UDK 620.172.2:621.3.015:519.7 ISSN 1580-2949
Original scientific article/Izvirni znanstveni ~lanek MTAEC9, 51(6)883(2017)
are filtered out, the slag metallic oxides are reduced, the
hydrogen and nitrogen are partly degassed, the melting
bath and temperature field are homogenized, and then
the formed slag exchange and the major alloying are
carried out. The melt is poured from the casting ladle
into the tundish on the continuous casting device, where
180 mm square billets from 2 m to 4 m in length are cast.
All billets are cooled before the rolling operation.
The billets are heated, in accordance with the pre-
scribed temperatures, in the continuous heating furnace.
After heating, the billets are hot rolled on the rolling
stands. Depending on the various shapes and dimensions
of the grooves, cylindrical, square or flat steel bars can
be produced. Steel bars are cooled on the cooling bed.
After cooling they are cut into different lengths using hot
shears. During cutting, the samples for examination of
the material are taken (e.g., tensile strength, hardenabi-
lity, micro-cleanliness).
After cooling, the bars can be additionally heat
treated in accordance with the customers’ orders.
In [tore Steel there are 7 different quality pres-
criptions (chemical compositions) for 16MnCrS5.19 Only
the most representative one (Table 1) was used in the
research. From October 2008 to February 2016, 70
consecutively cast batches (orders) with diameters from
20 mm to 55 mm were produced. In all cases the data on
tensile strength and elongation is available. For some
orders the bars were also heat treated:
• GKZ (Glühen auf kugeligen Zementit) – spheroidiza-
tion annealing or
• BG annealing – isothermal annealing (for excellent
machinability properties and a better micro-structure
homogenizing).
• The heat treatment is conducted using the tunnel
heat-treatment furnace.3
Table 1: Chemical composition in mass fractions (w/%) of the most
representative quality prescription (17) for 16MnCrS5
C Mn Cr S Mo Ni
Mini-
mum 0.14 1.00 0.8 0.020
Maxi-
mum 0.19 1.30 1.1 0.040 0.08 0.30
During the microstructure examination, also the pear-
lite content and percentage of spheroidization (after
GKZ annealing) were assessed. The micro-cleanliness
(K3) was determined according to DIN 50602, method
K. All the metallurgical examinations and tensile testing
were conducted by the head of the metallurgical labora-
tory and by the person responsible for mechanical
testing. The collected data is presented in Table 2. In the
same table, besides quantitative (i.e., chemical compo-
sition, content of pearlite, percentage of spheroidization
and micro-cleanliness), also the qualitative parameter is
included (heat treatment), where the prediction of
material properties is often aggravated.1,10
Table 2: The collected data on 16MnCrS5 (quality prescription 17)
B
at
ch
#
C
(%
)
M
n
(%
)
S
(%
)
C
r
(%
)
K
3
P
ea
rl
it
e
(%
)
S
ph
er
od
iz
at
i
on
(%
)
H
ea
t
tr
ea
tm
en
t
R
m
(N
/m
m
2 )
A
(%
)
1 0.17 1.26 0.03 1 7 40 0 / 669 24.5
2 0.17 1.22 0.027 0.99 5 40 0 / 604 17.2
3 0.17 1.22 0.027 0.99 6 45 0 / 658 16.1
4 0.18 1.2 0.025 0.97 6 40 0 / 654 16.2
5 0.17 1.19 0.023 0.99 7 45 0 / 666 16.8
6 0.19 1.29 0.023 1.09 9 40 90 GKZ 437 29.5
7 0.15 1.05 0.033 0.82 3 45 80 GKZ 454 30.4
8 0.15 1.02 0.028 0.82 11 40 90 GKZ 440 28.9
9 0.19 1.3 0.019 1.06 9 40 95 GKZ 446 30.5
10 0.19 1.27 0.021 1.07 15 40 0 BG 570 22.2
11 0.15 1.02 0.03 0.85 7 40 0 BG 593 22.3
… … … … … … … … … … …
70 0.16 1.09 0.029 0.92 5 40 0 / 644 15.2
3 MODELING OF TENSILE STRENGTH AND
ELONGATION
In Table 2 the percentage of spheroidization depends
on GKZ (Glühen auf kugeligen Zementit) – spheroidi-
zation annealing. Accordingly, both "qualitative" para-
meters of GKZ and BG annealing can be replaced,
respectively, by the quantitative values 0 and 1 (0 for
when no heat treatment is used; 1 for when the heat
treatment is used).
For the model fitness the average relative deviation
between the predicted and the experimental data was
selected. It is defined as Equation (1):
Δ =
−
=
∑ E P
n
i i
i
n
1
(1)
where n is the size of the collected data, and Ei and Pi
are the actual and the predicted total tensile strength or
elongation, respectively.
The tensile strength and elongation were modeled
using linear regression and genetic programming. The
results of the modeling are presented hereafter.
3.1 Linear regression
Based on the linear regression results regarding
tensile strength only the percent of spheroidization and
heat treatment are statistically significant influential
parameters (p < 0.05). The average relative deviation
between the predicted and the experimental data is
3.06 %. The linear-regression model for the tensile-
strength prediction is in Equation (2):
Rm = 500.0126·C – 198.777·Mn + 119.686·S +
128.071·Cr – 0.0738·K3 + 1.815·Pearlite –
1.66·Spherodization + 587.2196 (2)
M. KOVA^I^ et al.: INCREASING THE TENSILE STRENGTH AND ELONGATION OF 16MnCrS5 STEEL ...
884 Materiali in tehnologije / Materials and technology 51 (2017) 6, 883–888
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967–2017) – 50 LET/50 YEARS
During the elongation modeling only the percentage
of spheroidization and heat treatment are statistically
significant influential parameters (p < 0.05). The average
relative deviation between the predicted and the experi-
mental data is 7.56 %. The linear regression model for
the tensile-strength prediction is:
A = –57.295·C + 21.749·Mn ± 44.973·S – 12.498·Cr –
0.0093·K3 – 0.0247·Pearliten + 0.07823·Spherodization
+ 16.718 (3)
It must be emphasized that the percentage of sphe-
roidization and heat treatment statistically significantly
influences both the tensile strength and the elongation.
However, it does so conversely (i.e., if tensile strength is
increased by the influence of both parameters, the
elongation is decreased and vice versa).
Figure 1 shows the calculated influences of indivi-
dual parameters on the tensile strength and elongation
using the developed models, while separately changing
the individual parameter within the range from Table 1.
It can be concluded that the C, Mn, S and Cr content, the
percentage of spheroidization and the heat treatment are
the most influential factors.
3.2 Genetic programming
Genetic programming has already been found useful
for several different applications in [tore Steel Ltd.3,6,18,19
Genetic programming is a population-based algorithm
that is similar to a genetic algorithm and many other
heuristic optimization techniques.20–23 In the present
paper 100 models for tensile strength and also for elon-
gation were obtained through a genetic programming
method. During the simulated evolution the organisms
(with basic ingredients – function and terminal genes)
are generated and afterwards changed through different
changing algorithms. The following function genes were
selected: addition (+), subtraction (-), multiplication (*)
and division (/). The selected terminal genes were:
weight percentage of C (C), Mn (MN), S (S), Cr (CR),
the micro-cleanliness K3 (K3) determined according to
DIN 50602, method K, content of pearlite in %
(PEARLITE), percentage of spheroidization (SPHER)
and heat treatment (HT).
The AutoLISP-based in-house genetic programming
system was run 100 times in order to develop 100
independent civilizations. Each run lasted approximately
4 h and 40 min on a 3.0-GHz processor with 4 GB of
RAM.
M. KOVA^I^ et al.: INCREASING THE TENSILE STRENGTH AND ELONGATION OF 16MnCrS5 STEEL ...
Materiali in tehnologije / Materials and technology 51 (2017) 6, 883–888 885
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967–2017) – 50 LET/50 YEARS
Figure 1: Calculated influences of individual parameters on the
tensile strength and elongation, while separately changing them within
the range from Table 2
Figure 2: Calculated influences of individual parameters on the ten-
sile strength and elongation, while separately changing them within
the range from Table 2
(4)
are filtered out, the slag metallic oxides are reduced, the
hydrogen and nitrogen are partly degassed, the melting
bath and temperature field are homogenized, and then
the formed slag exchange and the major alloying are
carried out. The melt is poured from the casting ladle
into the tundish on the continuous casting device, where
180 mm square billets from 2 m to 4 m in length are cast.
All billets are cooled before the rolling operation.
The billets are heated, in accordance with the pre-
scribed temperatures, in the continuous heating furnace.
After heating, the billets are hot rolled on the rolling
stands. Depending on the various shapes and dimensions
of the grooves, cylindrical, square or flat steel bars can
be produced. Steel bars are cooled on the cooling bed.
After cooling they are cut into different lengths using hot
shears. During cutting, the samples for examination of
the material are taken (e.g., tensile strength, hardenabi-
lity, micro-cleanliness).
After cooling, the bars can be additionally heat
treated in accordance with the customers’ orders.
In [tore Steel there are 7 different quality pres-
criptions (chemical compositions) for 16MnCrS5.19 Only
the most representative one (Table 1) was used in the
research. From October 2008 to February 2016, 70
consecutively cast batches (orders) with diameters from
20 mm to 55 mm were produced. In all cases the data on
tensile strength and elongation is available. For some
orders the bars were also heat treated:
• GKZ (Glühen auf kugeligen Zementit) – spheroidiza-
tion annealing or
• BG annealing – isothermal annealing (for excellent
machinability properties and a better micro-structure
homogenizing).
• The heat treatment is conducted using the tunnel
heat-treatment furnace.3
Table 1: Chemical composition in mass fractions (w/%) of the most
representative quality prescription (17) for 16MnCrS5
C Mn Cr S Mo Ni
Mini-
mum 0.14 1.00 0.8 0.020
Maxi-
mum 0.19 1.30 1.1 0.040 0.08 0.30
During the microstructure examination, also the pear-
lite content and percentage of spheroidization (after
GKZ annealing) were assessed. The micro-cleanliness
(K3) was determined according to DIN 50602, method
K. All the metallurgical examinations and tensile testing
were conducted by the head of the metallurgical labora-
tory and by the person responsible for mechanical
testing. The collected data is presented in Table 2. In the
same table, besides quantitative (i.e., chemical compo-
sition, content of pearlite, percentage of spheroidization
and micro-cleanliness), also the qualitative parameter is
included (heat treatment), where the prediction of
material properties is often aggravated.1,10
Table 2: The collected data on 16MnCrS5 (quality prescription 17)
B
at
ch
#
C
(%
)
M
n
(%
)
S
(%
)
C
r
(%
)
K
3
P
ea
rl
it
e
(%
)
S
ph
er
od
iz
at
i
on
(%
)
H
ea
t
tr
ea
tm
en
t
R
m
(N
/m
m
2 )
A
(%
)
1 0.17 1.26 0.03 1 7 40 0 / 669 24.5
2 0.17 1.22 0.027 0.99 5 40 0 / 604 17.2
3 0.17 1.22 0.027 0.99 6 45 0 / 658 16.1
4 0.18 1.2 0.025 0.97 6 40 0 / 654 16.2
5 0.17 1.19 0.023 0.99 7 45 0 / 666 16.8
6 0.19 1.29 0.023 1.09 9 40 90 GKZ 437 29.5
7 0.15 1.05 0.033 0.82 3 45 80 GKZ 454 30.4
8 0.15 1.02 0.028 0.82 11 40 90 GKZ 440 28.9
9 0.19 1.3 0.019 1.06 9 40 95 GKZ 446 30.5
10 0.19 1.27 0.021 1.07 15 40 0 BG 570 22.2
11 0.15 1.02 0.03 0.85 7 40 0 BG 593 22.3
… … … … … … … … … … …
70 0.16 1.09 0.029 0.92 5 40 0 / 644 15.2
3 MODELING OF TENSILE STRENGTH AND
ELONGATION
In Table 2 the percentage of spheroidization depends
on GKZ (Glühen auf kugeligen Zementit) – spheroidi-
zation annealing. Accordingly, both "qualitative" para-
meters of GKZ and BG annealing can be replaced,
respectively, by the quantitative values 0 and 1 (0 for
when no heat treatment is used; 1 for when the heat
treatment is used).
For the model fitness the average relative deviation
between the predicted and the experimental data was
selected. It is defined as Equation (1):
Δ =
−
=
∑ E P
n
i i
i
n
1
(1)
where n is the size of the collected data, and Ei and Pi
are the actual and the predicted total tensile strength or
elongation, respectively.
The tensile strength and elongation were modeled
using linear regression and genetic programming. The
results of the modeling are presented hereafter.
3.1 Linear regression
Based on the linear regression results regarding
tensile strength only the percent of spheroidization and
heat treatment are statistically significant influential
parameters (p < 0.05). The average relative deviation
between the predicted and the experimental data is
3.06 %. The linear-regression model for the tensile-
strength prediction is in Equation (2):
Rm = 500.0126·C – 198.777·Mn + 119.686·S +
128.071·Cr – 0.0738·K3 + 1.815·Pearlite –
1.66·Spherodization + 587.2196 (2)
M. KOVA^I^ et al.: INCREASING THE TENSILE STRENGTH AND ELONGATION OF 16MnCrS5 STEEL ...
884 Materiali in tehnologije / Materials and technology 51 (2017) 6, 883–888
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967–2017) – 50 LET/50 YEARS
During the elongation modeling only the percentage
of spheroidization and heat treatment are statistically
significant influential parameters (p < 0.05). The average
relative deviation between the predicted and the experi-
mental data is 7.56 %. The linear regression model for
the tensile-strength prediction is:
A = –57.295·C + 21.749·Mn ± 44.973·S – 12.498·Cr –
0.0093·K3 – 0.0247·Pearliten + 0.07823·Spherodization
+ 16.718 (3)
It must be emphasized that the percentage of sphe-
roidization and heat treatment statistically significantly
influences both the tensile strength and the elongation.
However, it does so conversely (i.e., if tensile strength is
increased by the influence of both parameters, the
elongation is decreased and vice versa).
Figure 1 shows the calculated influences of indivi-
dual parameters on the tensile strength and elongation
using the developed models, while separately changing
the individual parameter within the range from Table 1.
It can be concluded that the C, Mn, S and Cr content, the
percentage of spheroidization and the heat treatment are
the most influential factors.
3.2 Genetic programming
Genetic programming has already been found useful
for several different applications in [tore Steel Ltd.3,6,18,19
Genetic programming is a population-based algorithm
that is similar to a genetic algorithm and many other
heuristic optimization techniques.20–23 In the present
paper 100 models for tensile strength and also for elon-
gation were obtained through a genetic programming
method. During the simulated evolution the organisms
(with basic ingredients – function and terminal genes)
are generated and afterwards changed through different
changing algorithms. The following function genes were
selected: addition (+), subtraction (-), multiplication (*)
and division (/). The selected terminal genes were:
weight percentage of C (C), Mn (MN), S (S), Cr (CR),
the micro-cleanliness K3 (K3) determined according to
DIN 50602, method K, content of pearlite in %
(PEARLITE), percentage of spheroidization (SPHER)
and heat treatment (HT).
The AutoLISP-based in-house genetic programming
system was run 100 times in order to develop 100
independent civilizations. Each run lasted approximately
4 h and 40 min on a 3.0-GHz processor with 4 GB of
RAM.
M. KOVA^I^ et al.: INCREASING THE TENSILE STRENGTH AND ELONGATION OF 16MnCrS5 STEEL ...
Materiali in tehnologije / Materials and technology 51 (2017) 6, 883–888 885
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967–2017) – 50 LET/50 YEARS
Figure 1: Calculated influences of individual parameters on the
tensile strength and elongation, while separately changing them within
the range from Table 2
Figure 2: Calculated influences of individual parameters on the ten-
sile strength and elongation, while separately changing them within
the range from Table 2
(4)
The selected maximum number of generations was
100. The selected size of the population of organisms
100, the reproduction probability 0.4, the crossover pro-
bability 0.6, the maximum permissible depth in the
creation of the population 6, the maximum permissible
depth after the operation of crossover 10, and the
smallest permissible depth of the organisms in gene-
rating new organisms 2. For the selection of organisms
the tournament method with tournament size 7 was used.
For the tensile-strength modeling, the most succesful
organism from all of the civilizations is presented in
Equation (4).
The average relative deviation between the predicted
and the experimental data is 2.89 %. The model consists
of 235 genes. Its depth is 14. It must also be emphasized
that the parameters (genes) CR, S, K3 are not included in
the model.
For elongation modeling, the most succesful organism
from all of the civilizations is presented in Equation (5).
The average relative deviation between the predicted
and the experimental data is 5.18 %. The model consists
of 189 genes. Its depth is 14. It must also be emphasized
that parameters (genes) CR, S, K3 are not included in the
model.
Figure 2 shows the calculated influences of the indi-
vidual parameters on the tensile strength and elongation
using the genetically developed model (Equations (4)
and (5)), while separately changing the individual para-
meter within the range from Table 2. It can be concluded
that Cr and pearlite content, the percentage of sphe-
roidization and the heat treatment are the most influential
factors.
4 IMPLEMENTATION OF MODELING RESULTS
Up until February 2016, six consecutively cast
batches (16MnCrS5, quality prescription 17) were used
for the implementation of the modeling results. The
collected data are presented in Table 3. Depending on
the chemical composition and the pearlite content, the
plan for the heat treatment is determined in order to
achieve the optimal tensile strength at moderate elon-
gation.
Table 3: The collected data on 6 16MnCrS5 (quality prescription 17)
consecutively cast batches
Batch #
C
(%)
Mn
(%)
S
(%)
Cr
(%)
K3
Pearlite
(%)
1 0.17 1.09 0.035 0.87 4 40
2 0.17 1.13 0.025 0.86 8 45
3 0.16 1.08 0.026 0.85 6 40
4 0.18 1.09 0.022 0.85 7 40
5 0.15 1.07 0.023 0.87 6 40
6 0.17 1.13 0.025 0.99 7 40
Figure 3 presents the prediction of tensile strength
using linear regression and genetic programming for the
6 batches (from Table 3). There are statistically signi-
ficant differences (one-way ANOVA, p < 0.05) between
the predicted tensile strength when the heat treatment is
used and when it is not used – for both methods: linear
regression and genetic programming. GKZ annealed
material has the statistically significantly lowest tensile
strength (one-way ANOVA, p < 0.05).
Figure 4 presents the prediction of elongation using
linear regression and genetic programming for 6 batches
(from Table 3). There are statistically significant diffe-
rences (one-way ANOVA, p < 0.05) between the predic-
ted elongation when the heat treatment is used and when
the heat treatment is not used – for both methods: linear
regression and genetic programming. GKZ annealed ma-
M. KOVA^I^ et al.: INCREASING THE TENSILE STRENGTH AND ELONGATION OF 16MnCrS5 STEEL ...
886 Materiali in tehnologije / Materials and technology 51 (2017) 6, 883–888
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967–2017) – 50 LET/50 YEARS
(5)
Figure 3: Calculated influences of the individual parameters on the
elongation, while separately changing them within the range from
Table 2
terial has the statistically significantly highest elongation
(one-way ANOVA, p < 0.05).
In accordance with the predictions presented in Fig-
ures 3 and 4 the BG annealing was selected in order to
achieve a higher tensile strength at moderate elongation.
The tensile-test results (tensile strength and elonga-
tion) and linear-regression results are presented in
Table 4.
The average relative deviation between the predicted
and the experimental data for the tensile strength for
linear regression and genetic programming is 3.55 % and
3.08 %, respectively. The average relative deviation bet-
ween the predicted and the experimental data for elon-
gation for linear regression and genetic programming is
3.56 % and 2.65 %, respectively.
5 CONCLUSIONS
The 16MnCrS5 steel grade is generally used for the
fabrication of case-hardened machine parts for several
applications (e.g., bars, rods, plates, strips, forgings),
where having a combination of wear resistance, tough-
ness and dynamic strength is essential. These qualities
can be easily correlated with tensile test results (e.g., ten-
sile strength, elongation), which depends on the chemi-
cal composition and the heat treatment after rolling.
Accordingly, from October 2008 to February 2016,
70 consecutively cast batches with diameters from
20 mm to 55 mm were produced. In all cases the data on
the tensile strength and elongation is available. For some
orders the bars were also heat treated:
• GKZ (Glühen auf kugeligen Zementit) – spheroidi-
zation annealing or
• BG annealing – isothermal annealing (for excellent
machinability properties and a better microstructure
homogenizing).
The chemical composition (content of C, Mn, S and
Cr) and heat-treatment regime (GKZ or BG annealing)
data were collected. During the microstructure examina-
tion also the pearlite content and the percentage of
spheroidization (after GKZ annealing) were assessed.
The micro-cleanliness (K3) was determined according to
DIN 50602, method K.
The tensile strength and elongation were modeled
using linear regression and genetic programming.
The ANOVA results obtained using linear regression
show that only the percentage of spheroidization and
heat treatment are statistically significant influential
parameters (p < 0.05) for tensile strength. The average
relative deviation between the predicted and the experi-
mental data is 3.06 %. At elongation only the percentage
of spheroidization and heat treatment are statistically
significant influential parameters (p < 0.05). The average
relative deviation between the predicted and the
experimental data is 7.56 %.
The average relative deviation between the predicted
and the experimental data for the best genetically deve-
loped mathematical model for predicting tensile strength
is 2.89 %. The model consists of 235 genes. Its depth is
14.
The average relative deviation between the predicted
and the experimental data for the best genetically deve-
loped mathematical model for predicting elongation is
5.18 %. The model consists of 189 genes. Its depth is 14.
Up until February 2016, six consecutively cast
batches (16MnCrS5, quality prescription 17) were used
for the implementation of the modeling results. To
achieve the optimal tensile strength at moderate elonga-
tion, the plan for the heat treatment has to be determined
on the basis of the chemical composition and the pearlite
content. The tensile strength and the elongation were
predicted using linear regression and genetic programm-
M. KOVA^I^ et al.: INCREASING THE TENSILE STRENGTH AND ELONGATION OF 16MnCrS5 STEEL ...
Materiali in tehnologije / Materials and technology 51 (2017) 6, 883–888 887
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967–2017) – 50 LET/50 YEARS
Figure 4: Calculated influences of the individual parameters on the
elongation, while separately changing them within the range from
Table 2
Table 4: The collected data on 6 16MnCrS5 (quality prescription 17) consecutively cast batches
B
at
ch
#
C
(%
)
M
n
(%
)
S
(%
)
C
r
(%
)
K
3
P
ea
rl
it
e
(%
)
R
m
(M
Pa
)
R
m
–
li
ne
ar
re
gr
es
si
on
(M
Pa
)
R
m
–
G
P
(M
Pa
)
A
(%
)
A
–
li
ne
ar
re
gr
es
si
on
(%
)
A
–
G
P
(%
)
1 0.17 1.09 0.035 0.87 4 40 622.0 600.28 590.53 22.60 21.82 16.85
2 0.17 1.13 0.025 0.86 8 45 641.0 598.63 599.92 22.00 23.10 14.02
3 0.16 1.08 0.026 0.85 6 40 578.0 593.48 590.74 21.90 22.81 16.91
4 0.18 1.09 0.022 0.85 7 40 613.0 600.94 590.54 23.00 22.05 17.10
5 0.15 1.07 0.023 0.87 6 40 607.0 592.67 590.98 22.70 23.05 16.89
6 0.17 1.13 0.025 0.99 7 40 598.0 606.28 589.95 22.30 21.61 17.05
The selected maximum number of generations was
100. The selected size of the population of organisms
100, the reproduction probability 0.4, the crossover pro-
bability 0.6, the maximum permissible depth in the
creation of the population 6, the maximum permissible
depth after the operation of crossover 10, and the
smallest permissible depth of the organisms in gene-
rating new organisms 2. For the selection of organisms
the tournament method with tournament size 7 was used.
For the tensile-strength modeling, the most succesful
organism from all of the civilizations is presented in
Equation (4).
The average relative deviation between the predicted
and the experimental data is 2.89 %. The model consists
of 235 genes. Its depth is 14. It must also be emphasized
that the parameters (genes) CR, S, K3 are not included in
the model.
For elongation modeling, the most succesful organism
from all of the civilizations is presented in Equation (5).
The average relative deviation between the predicted
and the experimental data is 5.18 %. The model consists
of 189 genes. Its depth is 14. It must also be emphasized
that parameters (genes) CR, S, K3 are not included in the
model.
Figure 2 shows the calculated influences of the indi-
vidual parameters on the tensile strength and elongation
using the genetically developed model (Equations (4)
and (5)), while separately changing the individual para-
meter within the range from Table 2. It can be concluded
that Cr and pearlite content, the percentage of sphe-
roidization and the heat treatment are the most influential
factors.
4 IMPLEMENTATION OF MODELING RESULTS
Up until February 2016, six consecutively cast
batches (16MnCrS5, quality prescription 17) were used
for the implementation of the modeling results. The
collected data are presented in Table 3. Depending on
the chemical composition and the pearlite content, the
plan for the heat treatment is determined in order to
achieve the optimal tensile strength at moderate elon-
gation.
Table 3: The collected data on 6 16MnCrS5 (quality prescription 17)
consecutively cast batches
Batch #
C
(%)
Mn
(%)
S
(%)
Cr
(%)
K3
Pearlite
(%)
1 0.17 1.09 0.035 0.87 4 40
2 0.17 1.13 0.025 0.86 8 45
3 0.16 1.08 0.026 0.85 6 40
4 0.18 1.09 0.022 0.85 7 40
5 0.15 1.07 0.023 0.87 6 40
6 0.17 1.13 0.025 0.99 7 40
Figure 3 presents the prediction of tensile strength
using linear regression and genetic programming for the
6 batches (from Table 3). There are statistically signi-
ficant differences (one-way ANOVA, p < 0.05) between
the predicted tensile strength when the heat treatment is
used and when it is not used – for both methods: linear
regression and genetic programming. GKZ annealed
material has the statistically significantly lowest tensile
strength (one-way ANOVA, p < 0.05).
Figure 4 presents the prediction of elongation using
linear regression and genetic programming for 6 batches
(from Table 3). There are statistically significant diffe-
rences (one-way ANOVA, p < 0.05) between the predic-
ted elongation when the heat treatment is used and when
the heat treatment is not used – for both methods: linear
regression and genetic programming. GKZ annealed ma-
M. KOVA^I^ et al.: INCREASING THE TENSILE STRENGTH AND ELONGATION OF 16MnCrS5 STEEL ...
886 Materiali in tehnologije / Materials and technology 51 (2017) 6, 883–888
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967–2017) – 50 LET/50 YEARS
(5)
Figure 3: Calculated influences of the individual parameters on the
elongation, while separately changing them within the range from
Table 2
terial has the statistically significantly highest elongation
(one-way ANOVA, p < 0.05).
In accordance with the predictions presented in Fig-
ures 3 and 4 the BG annealing was selected in order to
achieve a higher tensile strength at moderate elongation.
The tensile-test results (tensile strength and elonga-
tion) and linear-regression results are presented in
Table 4.
The average relative deviation between the predicted
and the experimental data for the tensile strength for
linear regression and genetic programming is 3.55 % and
3.08 %, respectively. The average relative deviation bet-
ween the predicted and the experimental data for elon-
gation for linear regression and genetic programming is
3.56 % and 2.65 %, respectively.
5 CONCLUSIONS
The 16MnCrS5 steel grade is generally used for the
fabrication of case-hardened machine parts for several
applications (e.g., bars, rods, plates, strips, forgings),
where having a combination of wear resistance, tough-
ness and dynamic strength is essential. These qualities
can be easily correlated with tensile test results (e.g., ten-
sile strength, elongation), which depends on the chemi-
cal composition and the heat treatment after rolling.
Accordingly, from October 2008 to February 2016,
70 consecutively cast batches with diameters from
20 mm to 55 mm were produced. In all cases the data on
the tensile strength and elongation is available. For some
orders the bars were also heat treated:
• GKZ (Glühen auf kugeligen Zementit) – spheroidi-
zation annealing or
• BG annealing – isothermal annealing (for excellent
machinability properties and a better microstructure
homogenizing).
The chemical composition (content of C, Mn, S and
Cr) and heat-treatment regime (GKZ or BG annealing)
data were collected. During the microstructure examina-
tion also the pearlite content and the percentage of
spheroidization (after GKZ annealing) were assessed.
The micro-cleanliness (K3) was determined according to
DIN 50602, method K.
The tensile strength and elongation were modeled
using linear regression and genetic programming.
The ANOVA results obtained using linear regression
show that only the percentage of spheroidization and
heat treatment are statistically significant influential
parameters (p < 0.05) for tensile strength. The average
relative deviation between the predicted and the experi-
mental data is 3.06 %. At elongation only the percentage
of spheroidization and heat treatment are statistically
significant influential parameters (p < 0.05). The average
relative deviation between the predicted and the
experimental data is 7.56 %.
The average relative deviation between the predicted
and the experimental data for the best genetically deve-
loped mathematical model for predicting tensile strength
is 2.89 %. The model consists of 235 genes. Its depth is
14.
The average relative deviation between the predicted
and the experimental data for the best genetically deve-
loped mathematical model for predicting elongation is
5.18 %. The model consists of 189 genes. Its depth is 14.
Up until February 2016, six consecutively cast
batches (16MnCrS5, quality prescription 17) were used
for the implementation of the modeling results. To
achieve the optimal tensile strength at moderate elonga-
tion, the plan for the heat treatment has to be determined
on the basis of the chemical composition and the pearlite
content. The tensile strength and the elongation were
predicted using linear regression and genetic programm-
M. KOVA^I^ et al.: INCREASING THE TENSILE STRENGTH AND ELONGATION OF 16MnCrS5 STEEL ...
Materiali in tehnologije / Materials and technology 51 (2017) 6, 883–888 887
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967–2017) – 50 LET/50 YEARS
Figure 4: Calculated influences of the individual parameters on the
elongation, while separately changing them within the range from
Table 2
Table 4: The collected data on 6 16MnCrS5 (quality prescription 17) consecutively cast batches
B
at
ch
#
C
(%
)
M
n
(%
)
S
(%
)
C
r
(%
)
K
3
P
ea
rl
it
e
(%
)
R
m
(M
Pa
)
R
m
–
li
ne
ar
re
gr
es
si
on
(M
Pa
)
R
m
–
G
P
(M
Pa
)
A
(%
)
A
–
li
ne
ar
re
gr
es
si
on
(%
)
A
–
G
P
(%
)
1 0.17 1.09 0.035 0.87 4 40 622.0 600.28 590.53 22.60 21.82 16.85
2 0.17 1.13 0.025 0.86 8 45 641.0 598.63 599.92 22.00 23.10 14.02
3 0.16 1.08 0.026 0.85 6 40 578.0 593.48 590.74 21.90 22.81 16.91
4 0.18 1.09 0.022 0.85 7 40 613.0 600.94 590.54 23.00 22.05 17.10
5 0.15 1.07 0.023 0.87 6 40 607.0 592.67 590.98 22.70 23.05 16.89
6 0.17 1.13 0.025 0.99 7 40 598.0 606.28 589.95 22.30 21.61 17.05
ing. Based on the obtained results the BG annealing was
selected in order to achieve a higher tensile strength at
moderate elongation.
The average relative deviation between the predicted
and the experimental data for the tensile strength for
linear regression and genetic programming is 3.55 % and
3.08 %, respectively. The average relative deviation bet-
ween the predicted and the experimental data for elon-
gation for linear regression and genetic programming is
3.56 % and 2.65 %, respectively.
In the future the proposed concept will be used for
analyzing the properties of selected steel grades.
6 REFERENCES
1 S. Bruschi, T. Altan, D. Banabic, P.F.Bariani, A. Brosius, J. Cao, A.
Ghiotti, M. Khraisheh, M. Merklein, A. E. Tekkaya, Testing and
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forming, CIRP Annals – Manufacturing Technology, 63 (2014) 2,
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Strength Steel with Tailored Properties by Two Methods, Procedia
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plified fuzzy logic approach for materials selection in mechanical
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doi:10.1016/j.matdes.2008.05.026
11 D. Pandya, D. Shah, Experimentation and its Prediction of Process
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Using ANN Model, Procedia Technology, 14 (2014), 282–289,
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12 I. Mohanty, D. Bhattacharjee, S. Datta, Designing cold rolled IF steel
sheets with optimized tensile properties using ANN and GA,
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15 J. Han, H. Li, Z. Zhu, L. Jiang, H. Xu, L. Ma, Effects of processing
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IJSIMM13(1)3.248
M. KOVA^I^ et al.: INCREASING THE TENSILE STRENGTH AND ELONGATION OF 16MnCrS5 STEEL ...
888 Materiali in tehnologije / Materials and technology 51 (2017) 6, 883–888
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967–2017) – 50 LET/50 YEARS
J. VANÌREK et al.: DURABILITY OF FRP/WOOD BONDS GLUED WITH EPOXY RESIN
889–895
DURABILITY OF FRP/WOOD BONDS GLUED WITH EPOXY
RESIN
OBSTOJNOST FRP/LESNIH SKLOPOV, LEPLJENIH Z EPOKSI
SMOLO
Jan Vanìrek, Milan [mak, Ivo Kusák, Petr Misák
Brno University of Technology, Faculty of Civil Engineering, Veveøí 95, 602 00 Brno, Czech Republic
vanerek.j@fce.vutbr.cz
Prejem rokopisa – received: 2016-11-18; sprejem za objavo – accepted for publication: 2017-04-20
doi:10.17222/mit.2016.321
The paper describes the properties of FRP/wood bonds, made of different wood species commonly used in the timber industry
within Central Europe (oak, spruce, pine and larch). An FRP fabric (glass-fiber-reinforced polymer – GFRP,
carbon-fiber-reinforced polymer – CFRP) was applied as the reinforcement. The durability of all the reinforced FRP/wood
assemblies was verified with short-term exposure tests following the tensile-shear-strength and wood-failure criteria for the
shear area of a single-lap joint. To precisely define the effect of the mechanical interlocking mechanism of the bond, analyses of
porosity and surface roughness, and SEM were performed.
Keywords: durability, epoxy, FRP, wood adherend
^lanek opisuje lastnosti FRP/lesnih sklopov, narejenih iz razli~nih vrst lesa, ki se obi~ajno uporablja v lesni industriji v Srednji
Evropi (hrast, jelka, bor, macesen). FRP-materiali (polimeri, oja~ani s steklenimi vlakni – angl. GFRP, polimeri, oja~ani z
ogljikovimi vlakni, angl. CFRP) so bili uporabljeni za oja~anje. Obstojnost vseh FRP/lesnih sklopov so preverjali s
kratkotrajnimi stri`nimi preizkusi. Kriterij je bil povr{ina stri`nega preloma na enojni prekrivni ploskvi med FRP in lesom. Da
bi lahko natan~no definirali mehansko trdnost spoja, so izvedli analize poroznosti, povr{inske hrapavosti in SEM-analize.
Klju~ne besede: trajnost, epoksi, FRP, lepljivost lesa
1 INTRODUCTION
The main purpose of the FRP usage with timber in
the construction industry is generally to improve the
stiffness/strength of reinforced items without any
influence on their service life or any environmental
impact. From the perspective of the timber-rein-
forcement process, the optimum dimensional stability
during moisture changes in wood should be one of the
most important criteria for such joints.
Different studies were made for the FRP/wood bond
durability using different types of adhesives. The
optimum results of adhesion were achieved using
formaldehyde-based adhesives;1–3 different, contrary
results were found using epoxy resins.4,5 The results1 for
the epoxy resins showed an inability to reach the
requirement for a cohesive failure of 80 % (in wood)
after an exposure to cyclic hygrothermal conditions.
Moreover, positive results were found using priming
treatments (hydroxymetyl resorcinol – HMR;
resorcin-fenol – RF; hexamethylolmelamin metyl ether –
MME) before the gluing process.2 The durability of
FRP/wood joints using epoxy has not been completely
established. Therefore, the final aim of this experiment
was to establish the durability aspect of such joints. The
focus was on the observation of the wood adhering
parameters having a significant impact on the mecha-
nical interlocking process to understand the impact of
mechanical interlocking on the joint durability.
2 EXPERIMENTAL PART
2.1 Materials
Carbon, glass or aramid are common reinforcement-
fiber materials used for external polymer-composite
systems known as fiber-reinforced polymers (FRPs).
Reinforcing of wood with FRP lamellas/fabrics could be
applied without any restriction regarding the wood
species. To evaluate the influence of wood species, the
experiment used glass-fiber-reinforced polymer (GFRP)
and carbon-fiber-reinforced polymer (CFRP) fabrics
applied to different wood adherents. Oak (Quercus
robur), spruce (Picea abies), pine (Pinus sylvestris) and
larch (Larix decidua) with a uniform thickness of 25 mm
were chosen. The fabrics with carbon fibers (Tyfo
SCH-41, FYFE Co.) with a width of 1.0 mm and the
GFRP with glass fibre (Tyfo-SEH-51A, FYFE Co.) with
a width of 1.3 mm were used. As the adhesive, the epoxy
resin TYFO S (diglycidyl ether of bisphenol-A,
DGEBA) with an amine hardener was used. Pre-treat-
ment including penetration of the wood surface to ensure
stabilization of wood cells (200 g/m2) was applied.
Similarly, the FRP fabrics were saturated with epoxy
resin (400 g/m2) on both sides. After 30 min of such
Materiali in tehnologije / Materials and technology 51 (2017) 6, 889–895 889
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967–2017) – 50 LET/50 YEARS
UDK 67.017:674-419.3:678.686 ISSN 1580-2949
Original scientific article/Izvirni znanstveni ~lanek MTAEC9, 51(6)889(2017)