Electro-Discharge Machining Advanced Materials under Low Frequency Vibrations: Modeling, Application, and Outlook

The material removal in Electro-Discharge Machining (EDM) occurs through the generation of high temperatures caused by intense electrical discharges, leading to the melting and vaporization of the workpiece and tool electrode. The ejected molten material solidifies in the dielectric liquid, forming debris that can significantly affect process accuracy, efficiency, productivity, and machinability if not effectively removed from the machining zone. The utilization of Low Frequency (LF) vibration (typically <1 kHz) to assist debris evacuation during Micro-EDM (µEDM) and EDM processes has emerged as a feasible solution. Moreover, the integration of powder into the dielectric medium (Powder mixed EDM, PMEDM) along with LF vibration presents an interactive approach to further enhance process performance. Despite its promise, the field lacks a unified understanding of LFV-EDM’s underlying mechanisms, systematic optimization frameworks, and clear pathways for industrial integration. This paper presents a comprehensive overview of research focusing on the influence of process parameters on key performance indicators such as Material Removal Rate (MRR), Electrode Wear Rate (EWR), surface roughness (Ra), and geometric accuracy in LF vibration-assisted µEDM and EDM. Various optimization methodologies, including statistical modeling, finite element analysis (FEA), computational fluid dynamics (CFD), and advanced techniques like Taguchi and artificial neural networks (ANN) employed in this field are extensively reviewed. Critical analysis of contradictory findings and material-specific responses is included. The review concludes with identified research gaps and prioritized future directions, including hybrid processes, advanced powder materials, and AI-driven optimization for LF- assisted µEDM and EDM processes. This work provides researchers with a consolidated knowledge base, a critical perspective on current limitations, and a prioritized agenda for future innovation, ultimately bridging the gap between laboratory research and scalable industrial application.