Multi-objective Optimization and Decision-Making Method for Laser Polishing Process Parameters of D2 Die Steel Based on NSGA-II and TOPSIS-EWM

To enhance the surface quality of D2 die steel after laser polishing, a multi-objective optimization and decision-making method for laser polishing process parameters was proposed. Three crucial process variables were laser power, scanning speed, and overlap rate. Three optimization targets were surface roughness, microhardness, and polishing depth. To establish a model with good prediction to describe the relationship between three process parameters and optimization targets, the prediction accuracies of four methods were compared. Among them, the response surface model (RSM) displayed the best prediction accuracy and was adopted as the optimized predictive model. Then, to obtain the optimal combination of process parameters, non-dominated sorting genetic algorithm II (NSGA-II) has been combined with the entropy-weight method technique for order preference by similarity to an ideal solution (EWM-TOPSIS). This approach aims to retain the maximum microhardness achievable while reducing the surface roughness. Following the optimization process, the optimal process parameter combinations were determined, represented as follows: laser power of 551 W, scanning speed of 9.0 mm s-1, and overlap rate of 0.43. The optimized surface roughness is Ra 1.351 mu m, which is 73.96% lower than the original surface roughness. The microhardness reached 512.1 HV0.5, which is 5.39% lower than the original microhardness. In addition, the polishing depth reached 0.305 mm.