Effect of laser parameters on surface texture of polyformaldehyde and parameter optimization - Li, Fengren (Strojniški vestnik, 2024)
This research aimed to investigate the influence of laser process parameters on the surface texture of Polyformaldehyde (POM) and to improve its processability and process predictability. A comparative experiment and analysis involving multiple processing parameters, including laser power, scanning speed, and pulse width, were conducted on POM. Statistical prediction models of laser processing POM were established among the laser power, scanning speed, pulse width, texture depth, surface roughness at the bottom of texture, and multi-objective optimization and experimental verification of process parameters were carried out based on the grey-Taguchi analysis method. Experimental results show that the laser power and scanning speed significantly affect the texture depth. Higher laser power and lower scanning speed are conducive to forming depth. The surface roughness at the bottom of the texture increases with the increase in scanning speed and shows a tendency to rise and then fall as the laser power increases. The surface roughness and texture depth obtained under the optimal process parameters(A5B1C1) are 1.373 µm and 466.891 µm, respectively, which were reduced by 10.08 % and increased by 3.42 % compared with the minimum surface roughness and maximum depth in the orthogonal experiments. The validation experiments of the prediction model show that it can meet the reliability requirements, and the errors of the predicted values of depth and surface roughness are 1.86 % and 7.60 %, respectively. The above research provides theoretical and experimental support for the precise control of surface texture prepared by laser processing POM.
Keywords: analitična metoda Grey-Taguchi; Grey-Taguchi analysis method; microstructures; mikroteksture; modeli za napovedovanje; obdelava s pikosekundnimi laserjji; optimizacija parametrov; parameters optimization; picosecond laser processing; poliformaldehidi; polyformaldehydes; prediction models