Simultaneous optimization of material removal rate and surface roughness in ultrasonic vibration-assisted EDM of HARDOX 500 using NSGA-II and MАВАС = Tối ưu hóa đồng thời năng suất bóc tách vật liệu và độ nhám bề mặt trong gia công tia lửa điện có hỗ trợ rung động siêu âm trên thép HARDOX 500 bằng phương pháp NSGA-II và MАВАС

Abstract

In this study, a multi-objective optimization framework is proposed for ultrasonic vibration-assisted electrical discharge machining (UV-EDM) of HARDOX 500 steel. The main objectives are to simultaneously maximize the material removal rate (MRR) and minimize the surface roughness (Ra), which represent productivity and surface quality, respectively. Experimental data were modeled using Gaussian process regression (GPR), and the optimization problem was solved with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate a Pareto front of trade-off solutions. To support decision-making, the Multi-Attributive Border Approximation area Comparison (MABAC) method was applied to select the most balanced solution from the Pareto set. The results demonstrate that the proposed NSGA-II-MABAC hybrid approach effectively identifies optimal machining parameters, providing a favorable compromise between high MRR and low Ra. The evolution of Pareto fronts across generations confirmed the convergence behavior of NSGA-II, while the knee-point analysis further validated the trade-off characteristics. This integrated framework offers a robust methodology for achieving simultaneous productivity and surface integrity in ultrasonic-assisted EDM of hard-to-machine materials such as HARDOX 500, and it can be extended to other advanced machining processes.