A multi-view deep learning approach for quality assessment in laser welding of hairpin windings based on 2D image captures

Abstract

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.