Experimental study and machining parameter optimization in milling thin-walled plates based on NSGA-II

The selection of machining parameters in milling thin-walled plates affects deformation, quality, and productivity of the machined parts. This paper presents an optimization procedure to determine and validate the optimum machining parameters in milling thin-walled plates. The regression models for cutting force and surface roughness are developed as objective functions according to experimental results. Besides, the influences of machining parameters on cutting force and surface roughness are also investigated. The objectives under investigation in this study are cutting force, surface roughness, and material removal rate subjected to constraints conditions. As the effects of milling parameters on optimization objectives are conflicting in nature, the multi-objective optimization problem in thin-walled plates milling is proposed. A non-dominated sorting genetic algorithm (NSGA-II) is then adopted to solve this multi-objective optimization problem. The optimized combinations of machining parameters are achieved by the Pareto optimal solutions, and these solutions are verified by the chatter stability.