Abstract
Electric Discharge Turning (EDT) is a hybrid non-traditional machining process suitable for intricate geometries and difficult-to-machine materials. In this study, EDT is conducted on Nimonic 80A alloy using a copper-tungsten (Cu-W) electrode with a specially designed setup that allows both rotation and feed of the cylindrical workpiece on a conventional EDM machine. An L27 orthogonal array-based experimental design is adopted, and Taguchi analysis is performed to evaluate process significance. Mathematical models for surface roughness (R a ), material removal rate (MRR), and tool wear rate (TWR) are formulated based on the experimental data. Regression models are developed and analyzed through ANOVA to identify significant factors. To optimize performance, multi-objective optimization framework leveraging non-dominated sorting genetic algorithm II (NSGA-II) for Pareto-based exploration and analytic hierarchy process (AHP) for post-processing decision support is utilized. The integration of AHP enables clear identification of the best trade-off solution based on decision-maker priorities. The results show that this hybrid method improves both machining efficiency and surface quality, offering better decision support than conventional optimization.
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