Multi-Objective Optimization of Surface Roughness and Material Removal Rate in Turning SKD11 Steel: A Combination of GP and MOPSO Algorithms

Abstract

This study investigates the optimization of surface roughness (Ra) and material removal rate (MRR) through experimental analysis and a combination of Genetic Programming (GP) and Multi-objective Particle Swarm Optimization (MOPSO) algorithms. A total of 65 experiments were conducted with different cutting parameters, including cutting speed (V), feed rate (f) and depth of cut (d). Analysis of Variance (ANOVA) results indicated that f significantly affects Ra, contributing to 87.46% of the total variance, while d and V had a lesser impact. For MRR, all three parameters showed significant effects, with d contributing 52.75% of the total variance. A predictive model was developed by the GP algorithm showed high accuracy with an R² of 0.978 for the training set and 0.934 for the validation set, demonstrating the model's reliability in predicting Ra values. The Pareto-optimal solutions of MOPSO showed stable convergence and identified a broad range of feasible solutions, with Ra values between 0.463 μm and 3.748 μm and MRR ranging from 213.723 mm³/min to 641.250 mm³/min. Validation experiments confirmed the accuracy of the optimization, with deviations between predicted and actual values of less than 8.43% for Ra and 0.05% for MRR. These results indicate that the proposed GP and MOPSO combination effectively optimizes machining processes, providing critical information about MRR and Ra in SKD11 steel turning.