This study investigates discharge-informed multi-objective optimization of wire electrical discharge machining of Inconel 718 using NSGA-II. Experiments based on an L18 orthogonal array were conducted to examine the influence of pulse parameters, wire feed, and servo voltage on surface roughness, kerf width, and cutting speed. Regression-based response models developed from the experimental data were used to generate Pareto-optimal machining conditions. A compromise solution, determined using a normalized distance criterion, was validated through confirmation experiments, showing good agreement between predicted and measured responses. Surface topography and microstructural observations indicated that discharge energy significantly influences crater morphology, where higher discharge energy produced larger craters and irregular molten regions, while the optimized condition resulted in moderate crater sizes and improved surface uniformity. Voltage–current signal analysis further indicated that the optimized machining condition corresponds to a balanced discharge regime characterized by stable discharge behavior and effective energy distribution. The compromise solution yielded surface roughness of 1.068 µm, kerf width of 310.73 µm, and cutting speed 0.991 mm/min, while confirmation experiments showed deviations of 1.03%, 1.51%, and 12.3%, respectively. The results demonstrate that integrating multi-objective optimization with discharge behavior analysis enables physically consistent selection of machining conditions in wire electrical discharge machining.
Kumar et al. (Sun,) studied this question.