Comprehensive Analysis of Milling Performance and Multi-Objective Parameter Optimization for YG6C Milling Tool

Numerous conflicting objectives exist in the engineering field, and resolving these conflicts to reduce costs constitutes a problem that demands top-priority consideration. A model for tool wear and a multi-quadratic regression model for milling forces were developed to accurately predict the trends of wear on the rake face of the milling tool and the variations in milling forces. The influence of milling parameters (spindle speed, n; feed rate, vf; axial milling depth, ap) on both the wear of the rake face and milling force was analyzed by means of orthogonal experiments. The findings indicated that the impact of these parameters on the wear ranked in the following order: n > vf > ap. In contrast, for milling force, F, the ranking was ap > vf > n. Utilizing MATLAB’s genetic algorithm, an optimization procedure was conducted with multiple objectives including the wear of the rake face, milling force, and material removal rate; subsequently, a Pareto optimal solution set was generated for milling parameters based on practical processing requirements.