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

In this chapter a methodology based on multi-objective particle swarm optimization algorithm to identify 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 objective functions to optimize. In addition, the proposal considered 4 constraints related to cutting tools, rotating speed of the main spindle, cutting depth per passing, and speed interval for advance. Once the objective functions and constraints were determined, two test workpieces with different geometries were designed by an experienced machinist by means of Solidworks® 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 multi-objective particle swarm optimization algorithm proposed was executed 50 times, and the parameters from the 2 and 3 best solutions were used to design 5 new workpieces. From the results obtained it was observed that the methodology proposed can support unexperienced operators in optimizing the parameters for machining. For the first workpiece the machining time was reduced 32.06%, material removal rate was increased 57.38%, and an increment of 14.06% was obtained for the feed rate. Whereas for the second workpiece the values obtained were 42.91, 50.47 and 30.83%, respectively. Multi-objective optimization procedure may be employed for machining parameter optimization of non-simple geometry workpieces.