Multi-objective optimization of cutting parameters for micro-milling nickel-based superalloy thin-walled parts based on improved NSGA-II algorithm

This paper focuses on the difficulties in high-quality and high-efficiency micro-milling nickel-based superalloy micro thin-walled parts. The second-generation Non-dominated Sorting Genetic Algorithm (NSGA-II) is improved. A central composite experiment is designed, and a surface roughness prediction model is developed for micro-milling thin-walled parts. A prediction model for surface residual stress on thin-walled parts is developed using an L9(34) orthogonal simulation experiment. Using the NSGA-II algorithm, the four cutting parameters (spindle speed, feed per tooth, axial cutting depth, and radial cutting depth) are optimized to achieve low surface roughness and high material removal rate, while stable cutting and surface compressive residual stress are considered constraints. Finally, the high-quality and high-efficiency micro-milling of the Inconel 718 cross-shaped thin-walled parts is realized.