An intelligent process parameters determination method based on multi-algorithm fusion: a case study in five-axis milling

Process parameters have a significant effect on surface integrity, which determines the service performance of the parts. To improve surface integrity, the process parameters are determined: 1) by experienced engineers directly, 2) based on the Pareto frontier automatically constructed by swarm intelligence algorithms. However, as the Pareto frontier contains many non-dominated solutions, the final parameters are still determined by experienced engineers, which reduces the intelligence level. Therefore, an intelligent process parameters determination method based on multi-algorithm fusion is proposed towards minimal surface residual stress in feed or transverse direction (Rsf, Rst) and surface roughness (Ra) in five-axis milling. Firstly, the Improved Generalized Regression Neural Network (IGRNN), which enhances the nonlinear mapping capability even in dealing with a small batch of experiments, is proposed to predict the Rsf, Rst, and Ra with certain inputs (including lead angle, tilt angle, cutting depth, feed speed, and spindle speed). Then based on the proposed model, the Improved Non-dominated Sorted Genetic Algorithm-II (INSGA-II), which improves the uniformity of the Pareto frontier, is used to obtain a series of non-dominated process parameters. Finally, the optimal parameters are determined by the Principal Component Analysis (PCA) without manual weight assignment for Rs and Ra. By comparing with the second-best one, although the Rsf decreases by 0.33%, which is still able to obtain negative residual stress, the Rst and Ra are greatly improved by 9.3% and 47.94%, respectively. The proposed method could improve the intelligent level of process parameters determination and the service performance of the parts. Furthermore, it lays a foundation for the realization of intelligent manufacturing.