{"id":3229,"date":"2022-07-13T09:30:00","date_gmt":"2022-07-13T07:30:00","guid":{"rendered":"https:\/\/cirpicme.org\/?page_id=3229"},"modified":"2022-07-26T13:05:43","modified_gmt":"2022-07-26T11:05:43","slug":"data-driven-quality-monitoring-of-needle-winding-processes-in-electric-motor-production-using-machine-learning-techniques","status":"publish","type":"page","link":"https:\/\/cirpicme.org\/index.php\/forming-welding-2\/data-driven-quality-monitoring-of-needle-winding-processes-in-electric-motor-production-using-machine-learning-techniques\/","title":{"rendered":"Data-driven quality monitoring of needle winding processes in electric motor production using machine learning techniques"},"content":{"rendered":"\n

by Andreas Mayr, Fabian Scheffler, Robert Fuder, Dominik Kisskalt, Tim Raffin, Joerg Franke (Germany)<\/em><\/p>\n\n\n\n

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Abstract<\/strong><\/p>\n\n\n\n

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 data analytics, in particular machine learning (ML), are recently moving into focus. Although needle winding represents the most widely used process for winding inner-slotted stators, the potential of ML has not yet been tapped. Therefore, this paper introduces an approach for a data-driven quality monitoring system for needle winding processes using ML techniques. To get the required quality-related process data, a common needle winding machine is initially be equipped with suitable sensors. 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.<\/p>\n\n\n\n

Keywords<\/strong>: Needle winding; Electric motor production; Data analytics; Machine learning; Quality monitoring; Industry 4.0<\/p>\n\n\n\n

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Video presentation<\/strong><\/p>\n\n\n\n