Effizientes Datenmanagement zur Klassifizierung elektrischer Signale in der Funkenerosion

Abstract

The integration of Artificial Intelligence (AI) into manufacturing processes offers significant potential for efficiency improvements, particularly in small and medium-sized enterprises (SMEs). The ProKI-Berlin project, as part of the nationwide ProKI network, facilitates the adoption of AI-driven methods in production technology by providing practical applications, training programs, and interdisciplinary research. This paper presents a novel methodology for real-time process monitoring and optimization in electrical discharge machining (EDM) using graphite electrodes. By leveraging real-time signal classification techniques, process anomalies can be detected early, enabling adaptive control and enhanced machining precision. The approach not only improves the understanding of discharge phenomena but also contributes to the development of sustainable and resource-efficient manufacturing solutions. The findings demonstrate the potential of AI in optimizing EDM processes and highlight broader implications for intelligent production systems.