Author:  Dinh Van Thanh, Le Thu Quy, Do Thi Tam, Vu Ngoc Pi, Tran Thi Phuong Thao

Place posted: International Journal of Mechanics, vol. 20, pp. 7-14, 2026. DOI: 10.46300/9104.2026.20.2 

Post time: 2026

Abstract: Ultrasonic-assisted Electrical Discharge Machining (UV-EDM) has emerged as a promising technique for improving machining efficiency and electrode life, particularly when processing hard conductive materials. This study presents a hybrid multi- criteria optimization framework that integrates Non- dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-Attributive Border Approximation Area Comparison (MABAC) method to optimize two conflicting performance measures: Material Removal Rate (MRR) and Electrode Wear Rate (EWR).
A Box–Behnken Design (BBD) experimental matrix was developed with five input parameters: ultrasonic vibration amplitude, pulse-on time, pulse-off time, peak current, and servo voltage. Experiments were conducted using a Sodick A30 EDM machine with a custom-designed ultrasonic horn and copper electrodes on 90CrSi tool steel. Surrogate models for MRR and EWR were constructed using Gaussian Process Regression (GPR), enabling efficient evaluation of process responses during the optimization phase.
NSGA-II was employed to generate a diverse Pareto front representing trade-offs between MRR and EWR.
Subsequently, the MABAC method was applied to rank the Pareto-optimal solutions and identify the most balanced process setting. The best compromise solution offered a high MRR while maintaining a low EWR, demonstrating the effectiveness of the proposed hybrid approach in balancing productivity and electrode sustainability in UV-EDM operations.

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