Optimization of Turning Process Parameters Using Genetic Algorithm for Enhanced Machining Performance

Optimization of machining parameters plays a crucial role in achieving higher productivity and superior product quality in today’s competitive manufacturing environment. This study focuses on the multi-objective optimization of the turning process for C45 medium carbon steel, targeting the simultaneous reduction of surface roughness (Ra) and enhancement of material removal rate (MRR) using Genetic Algorithms (GA). Cutting speed, feed rate, and depth of cut were selected as the primary process parameters. A total of eighteen experiments were conducted using Taguchi’s L18 (3³·2¹) orthogonal array, and the collected data was further analyzed and modeled using the Response Surface Methodology (RSM). The influence and significance of each machining parameter on the overall performance characteristics were also examined through RSM. Genetic Algorithms were then employed to determine the optimal combination of parameters. The optimized results were validated through confirmation experiments, demonstrating strong agreement with the predicted values and confirming the effectiveness of the proposed approach.