In-situ process monitoring and control in EDM: a reviewYe, Long (Avtor) Guo, Cheng (Avtor) Valentinčič, Joško (Avtor) Qian, Jun (Avtor) Reynaerts, Dominiek (Avtor) Yu, Nan (Avtor) EDMprocess monitoringprocess controlmachine learningElectrical discharge machining (EDM) is a well-established technique to process challenging materials such as hardened steel, superalloys, and metal matrix composites, irrespective of their mechanical properties. However, the complex interactions among machining parameters and spatio-temporal process phenomena complicates the quality assurance in EDM, particularly for intricate features or mass production requirements. In-situ process monitoring and control (PMC) emerges as an effective method to mitigate the complexity, achieving stable discharge process and high-quality as-machined parts. This paper presents a comprehensive review of state-of-the-art PMC strategies, addressing their key elements and challenges in the context of EDM. Various sensor-based monitoring including electrical, acoustic emission and process force signals together with high-speed imaging monitoring are examined for their capabilities and limitations in discovering the gap phenomena and their potential for industrial applications. Specifically, emerging machine learning (ML) techniques are highlighted for their application to process temporal signals and identify underlying discharge conditions. This paper also discusses advances in monitoring-based closed-loop feedback control, addressing their effects for prompt adjustment of discharge gap width and long-time process stability. Future research directions such as multi-modal sensor fusion, AI-integrated control and digital twin are proposed towards achieving efficient, reliable, and intelligent PMC with a target at high-level industrial readiness.20252025-10-15 12:06:26Članek v reviji175086UDK: 621.9.048:004.85ISSN pri članku: 1526-6125DOI: 10.1016/j.jmapro.2025.08.031COBISS_ID: 246767363sl