Abstract

This study presents a novel hybrid optimization framework that integrates Genetic Algorithms (GA) with Response Surface Methodology (RSM) for optimizing machining parameters in end-milling operations, specifically aimed at minimizing surface roughness. The proposed GA–RSM framework overcomes the limitations of traditional methods by combining the global search ability of GA with the predictive modeling power of RSM. A second-order polynomial regression model was developed using a full-factorial experimental design (27 trials) on aluminum alloy specimens and embedded within a GA loop featuring adaptive mutation decay and tournament selection to promote robust convergence. Experimental validation demonstrated that the proposed approach reduced surface roughness by 9.5% relative to Gradient Descent, 11.8% compared to Simulated Annealing, and 18.8% compared to manual parameter selection, achieving a minimum roughness of 13.4 µin. The framework maintains computational efficiency and offers extensibility to other machining processes and materials. It delivers a reproducible, statistically validated, and practically feasible solution for surface roughness optimization, with direct applications in aerospace, automotive, and precision manufacturing sectors.

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