Kurzfassung
Industrie 4.0 geht mit einer Vielzahl an Technologien einher, die großes Potenzial für die Elektromotorenproduktion von morgen bieten. Vor allem datengetriebene Ansätze, die sich der Methoden des maschinellen Lernens (ML) bedienen, rücken zunehmend in den Fokus. Im Rahmen dieses Beitrags wird gezeigt, wie sich die daraus ergebenden Potenziale systematisch erschließen lassen. Nach einem kurzen Überblick über bestehende ML-Ansätze in der Elektromotorenproduktion wird anhand des Referenzmodells CRISP-DM beispielhaft eine ML-basierte Qualitätsüberwachung für das Laserschweißen von Hairpins entwickelt.
Abstract
Industry 4.0 is accompanied by a multitude of technologies that offer great potential for tomorrow's electric motor production. Especially data-driven approaches such as machine learning (ML) are increasingly coming to the fore. This article shows how the resulting potentials can be tapped systematically. After a brief overview of existing ML approaches in electric motor production, an ML-based quality monitoring system for the laser welding of hairpins is developed exemplarily following the reference model CRISP-DM.
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