Electrical discharge machining represents a non-conventional machining process, specifically designed for the effective fabrication of materials that are difficult to machine and for components with complex geometries. Many studies have been carried out that combine electrical discharge machining with the ultrasonic vibration of electrodes. Nevertheless, most of these investigations have concentrated on the processing of hole or cavity components. This document presents an experimental study focused on the design of an electrode holder for ultrasonic vibration electrical discharge machining, focusing on the machining of cylindrical surfaces. This study involved a two-stage design process for the electrode holder, aimed at determining the optimal length to achieve the maximal material removal rate and to ensure surface roughness. The novel aspect of this study is that it is the first to be published on the use of ultrasonic vibration in the electrical discharge machining process for processing cylindrical surfaces. Furthermore, splitting the electrode holder design process into two stages (theoretical calculation and experimental determination) made it possible to identify an electrode holder design for ultrasonic vibration electrical discharge machining that increased the MRR by 35.5% while maintaining SR values that were similar to those produced during the electrical discharge machining without ultrasonic vibration.

Ultrasonic Vibration-Assisted Electrical Discharge Machining (UV-EDM) constitutes a promising technique for improving machining efficiency and surface quality, particularly when working with difficult-to-machine materials. This study presents a comprehensive multi-objective optimization approach for UV-EDM applied to 90CrSi steel, aiming to maximize the Material Removal Rate (MRR) while minimizing Surface Roughness (Ra). The experimental data were collected under varying process parameters, including the peak current, pulse-on time, and ultrasonic vibration amplitude. A Gaussian Process Regression (GPR) model was developed to accurately predict MRR and Ra. These predictive models were then integrated into the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to perform Pareto-based optimization. Additionally, the Analytic Hierarchy Process (AHP) was employed to rank the Pareto-optimal solutions based on decision-makers’ preferences. The results demonstrate the effectiveness of combining GPR and NSGA-II for modeling and optimizing UV-EDM, while the use of AHP enables a rational selection of optimal machining conditions. This hybrid methodology offers valuable insights into enhancing productivity and surface integrity in precision machining applications.