A Methodology for Optimizing the Process of Machining a Workpiece Using Multi-Objective Particle Swarm Optimization

In this paper a methodology based on multi-objective particle swarm optimization algorithm, for identifying the optimal parameters for machining a workpiece with a milling is presented. The time for machining, the material removal rate, and the feed rate were identified as the objectives to optimize. In addition, the proposal considered 4 constraints related to cutting tools, rotating speed of main spindle, cutting depth per passing, and speed interval for advance. Once the objective functions and constraints were determined, the test workpiece was designed by an unexperienced machinist by means of CATIA software, and then exported to Mastercam X in order to generate the G & M codes. The material selected for machining was delrin. In the experimentation stage, the methodology proposed was executed 50 times, and the parameters from the 2 best solutions were used to design 2 new workpieces. From the results obtained it was observed that the methodology proposed can support unexperienced operators in optimizing the parameters for machining. The machining time was reduced in 30%, material removal rate was increased about 55%, and an increment of 14% was obtained for the feed rate.