Data-driven quality monitoring of needle winding processes in electric motor production using machine learning techniques
Mayr A, Scheffler F, Fuder R, Raffin T, Kißkalt D, Franke J (2023)
Publication Type: Conference contribution
Publication year: 2023
Journal
Publisher: Elsevier B.V.
Book Volume: 118
Pages Range: 873-878
Conference Proceedings Title: Procedia CIRP
Event location: Naples, ITA
DOI: 10.1016/j.procir.2023.06.150
Abstract
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.
Authors with CRIS profile
Andreas Mayr
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik
Fabian Scheffler
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik
Tim Raffin
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik
Dominik Kißkalt
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik
Jörg Franke
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik
Involved external institutions
Germany (DE)How to cite
APA:
Mayr, A., Scheffler, F., Fuder, R., Raffin, T., Kißkalt, D., & Franke, J. (2023). Data-driven quality monitoring of needle winding processes in electric motor production using machine learning techniques. In Roberto Teti, Doriana D'Addona (Eds.), Procedia CIRP (pp. 873-878). Naples, ITA: Elsevier B.V..
MLA:
Mayr, Andreas, et al. "Data-driven quality monitoring of needle winding processes in electric motor production using machine learning techniques." Proceedings of the 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2022, Naples, ITA Ed. Roberto Teti, Doriana D'Addona, Elsevier B.V., 2023. 873-878.
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