Multi-Objective Optimization of Wire Electrical Discharge Machining Parameters in Machining of Inconel 625 Using Bio-Inspired Optimization Algorithms
Abstract
Wire Electrical Discharge Machining (WEDM) is highly significant for machining Inconel, which is widely used across industries for its excellent strength and heat resistance. Traditional machining of Inconel poses several difficulties; therefore, WEDM becomes a better option. One of the major challenges in WEDM is achieving optimal Material Removal Rate (MRR) and Surface Roughness (SR) simultaneously. In the present investigation, WEDM operation was conducted on Inconel 625 using a molybdenum wire as the electrode material, with three essential process variables: Pulse ON Time (PON), Pulse OFF Time (POFF), and peak current (IP). Multi-response optimization was performed using Response Surface Methodology (RSM) with the Face-centred Central Composite (FCC) design, the Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Particle Swarm Optimization (PSO). The optimal values obtained using RSM were 45.71 µs for PON, 8.02 µs for POFF, and 4.78 A for IP, providing an SR of 3.997 µm and an MRR of 5.612 mm3/min. PSO focused more on the quality of the processed surface and gave a better SR of 3.098 µm, but the resulting MRR was 4.437 mm3/min using 20 µs for PON, 12 µs for POFF, and 3 A for IP. Conversely, the NSGA-II optimized for maximal machining productivity and generated the highest MRR of 5.907 mm3/min with an SR of 3.867 µm using 49.42 µs for PON, 8 µs for POFF, and 5 A for IP. Novelty lies in the use of statistics and metaheuristics to compare the performance of optimization methods in nonlinear parameter response analysis. Although RSM demonstrated high predictive accuracy, both the NSGA-II and PSO improved the search for the trade-off between MRR and SR. Overall, the best compromise between MRR and SR was achieved using the solution based on the NSGA-II results. The obtained results will help in developing advanced machining technologies aimed at precision processing of superalloys that contribute to SDG 9—Industry, Innovation and Infrastructure.