Mayr A, Bauer J, Franke J (2022)
Publication Type: Journal article
Publication year: 2022
Original Authors: Andreas Mayr, Johannes Bauer, Jörg Franke
Book Volume: 115
Pages Range: 196-201
DOI: 10.1016/j.procir.2022.10.073
Electric traction motors must meet high requirements in terms of efficiency in driving operation as well as cost-effectiveness in manufacturing. The so-called hairpin technology yields potential for both aspects. However, a central bottleneck in hairpin stator manufacturing is the contacting process, commonly performed by laser welding, where a large number of joints must be created. Since a single defective weld can lead to a failure of the whole stator, effective methods for quality assessment are needed. Building on preliminary work, this paper proposes a novel multi-view deep learning architecture for combined processing of pre- and post-process images to detect welded joints with an insufficient cross-section.
APA:
Mayr, A., Bauer, J., & Franke, J. (2022). A multi-view deep learning approach for quality assessment in laser welding of hairpin windings based on 2D image captures. Procedia CIRP, 115, 196-201. https://dx.doi.org/10.1016/j.procir.2022.10.073
MLA:
Mayr, Andreas, Johannes Bauer, and Jörg Franke. "A multi-view deep learning approach for quality assessment in laser welding of hairpin windings based on 2D image captures." Procedia CIRP 115 (2022): 196-201.
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