Data-driven quality monitoring of bending processes in hairpin stator production using machine learning techniques
Mayr A, Röll P, Winkle D, Enzmann M, Bickel B, Franke J (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Elsevier B.V.
Book Volume: 103
Pages Range: 256-261
Conference Proceedings Title: Procedia CIRP
Event location: Virtual, Online
DOI: 10.1016/j.procir.2021.10.041
Abstract
In the course of series production of high-performance automotive traction motors, the so-called hairpin technology is increasingly coming into focus. An essential process step is the bending of the hairpins, which is significantly influenced by variations in the wire material. Therefore, this paper will investigate how machine learning techniques can be used to monitor the dimensional accuracy of hairpins solely based on bending process data such as torque curves. In this way, deviations can be detected at an early stage and measures can be taken to prevent further rejects.
Authors with CRIS profile
Andreas Mayr
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik
Philipp Röll
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik
Jörg Franke
Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik
Involved external institutions
Germany (DE)How to cite
APA:
Mayr, A., Röll, P., Winkle, D., Enzmann, M., Bickel, B., & Franke, J. (2021). Data-driven quality monitoring of bending processes in hairpin stator production using machine learning techniques. In Khaled Medini, Thorsten Wuest (Eds.), Procedia CIRP (pp. 256-261). Virtual, Online: Elsevier B.V..
MLA:
Mayr, Andreas, et al. "Data-driven quality monitoring of bending processes in hairpin stator production using machine learning techniques." Proceedings of the 9th CIRP Global Web Conference on Sustainable, Resilient, and Agile Manufacturing and Service Operations: Lessons from COVID-19, CIRPe 2021, Virtual, Online Ed. Khaled Medini, Thorsten Wuest, Elsevier B.V., 2021. 256-261.
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