Optimization of CNC Face Milling Process Parameters using Response Surface Methodology and Particle Swarm Optimization Algorithm

In this research, multi-objective type of the Particle Swarm optimization algorithm, has been applied, for solving real world's complex problem. In this experimental research, parametric multi-objective optimization of computer numerical control (CNC) face milling on En-8 steel has been done using multi-objective Particle Swarm optimization method. The goal of this investigation is to optimize the process parameters like feed rate, depth of cut, spindle speed for responses like Material Removal Rate (MRR) and Surface Roughness (SR). A Full Factorial Design for three process parameters of three levels each, were selected to plan the experiments. Analysis of Variance (ANOVA) found significant process parameters affecting responses and also check validity of derived modal. In this work, multi-objective type of Particle Swarm Optimization (PSO) was also compared with single objective Particle Swarm optimization and obtained effective difference between both of the optimization results.