Data-driven quality monitoring of needle winding processes in electric motor production using machine learning techniques

Industry 4.0 is accompanied by various technologies, which offer great potential for optimizing today's manufacturing of electric motors. To meet high-quality standards and to enable further improvements, methods from artificial intelligence, in particular machine learning (ML), are recently moving into focus. Although needle winding is one of the most widely used processes for winding stators with inner slots, the potential of ML has not yet been tapped. Therefore, this paper introduces a novel, data-driven approach for quality monitoring of needle winding processes using ML techniques. To acquire quality-related process data, a common needle winding machine is equipped with suitable sensors first. Based on this, a proof of concept for an ML-based regression model aiming at predicting the coil quality solely based on process data is provided.