Multi-Objective Optimization of EDM Parameters for 304 Stainless Steel Considering Material Removal Rate and Surface Roughness Using RBF-Kriging and NSGA-II

To balance machining efficiency and surface quality in the electrical discharge machining (EDM) of 304 stainless steel, this study investigates the effects of peak current, pulse duration, and pulse interval on material removal rate (MRR) and surface roughness (Ra). Orthogonal experiments, optimal Latin hypercube sampling, grey relational analysis, and single-factor experiments were employed to characterize the EDM process and clarify the effects of electrical parameters on surface morphology and roughness. The results indicate that peak current has the most significant influence on both MRR and Ra, while changes in electrical parameters affect Ra through discharge intensity, thermal input, and discharge crater characteristics. A hybrid RBF-Kriging surrogate model was then developed to describe the nonlinear relationship between process parameters and responses, achieving R2 values of 0.9773 for MRR and 0.9874 for Ra. Under a unified repeated 5-fold cross-validation and LOOCV protocol, this model performed comparably to quadratic RSM, GPR, SVR and ANN (all R2 > 0.95). NSGA-II was subsequently used to obtain the Pareto-optimal solutions, and equal-weight TOPSIS was applied to determine a compromise solution. The optimal parameters were a peak current of 5.78 A, pulse duration of 47 μs, and pulse interval of 22 μs, yielding predicted MRR and Ra values of 3.338 mm3/min and 3.936 μm, respectively. Experimental verification produced MRR and Ra values of 3.566 mm3/min and 4.054 μm, with prediction errors of 6.81% and 2.98%, respectively. The results demonstrate that the proposed RBF-Kriging-NSGA-II framework provides an effective approach for balancing machining efficiency and surface quality in EDM of 304 stainless steel.