Enhancing WEDM Efficiency by Teaching–Learning-Based Optimization for Machining Process Parameter Optimization

Modern machining techniques like wire electrical discharge machining (WEDM) enable the cutting of complicated shapes. Parameter optimization is necessary during the machining process of titanium alloy. This optimization may help in cost reduction in machining. The objective of this study is to determine the best parameters for machining processes based on single and multi-objective optimization. The study focused on three machining responses: material removal rate (MRR), gap size (GS), and surface roughness (Ra) in the EDM machining process. To achieve the most optimal outcome, teaching–learning-based optimization (TLBO) was employed by comparing the results obtained from optimized data with experimental data based on WEDM parameters. The optimization using TLBO demonstrated that the optimized data produced better results than the existing experimental data for MRR, GS, and Ra. This method is superior and more efficient than the traditional approach used for parameter optimization in machining processes. It is specifically designed to optimize the parameters of the EDM machine learning process.