Camera-based spatter detection in laser welding with a deep learning approach
Hartung, Julia
; Jahn, Andreas; Stambke, Martin; Wehner, Oliver; Thieringer, Rainer; Heizmann, Michael
; Jahn, Andreas; Stambke, Martin; Wehner, Oliver; Thieringer, Rainer; Heizmann, MichaelAbstract:
Laser welding, semantic segmentation, u-net, quality assurance, spatter detection
| Zugehörige Institution(en) am KIT | Institut für Industrielle Informationstechnik (IIIT) |
| Publikationstyp | Proceedingsbeitrag |
| Publikationsmonat/-jahr | 11.2020 |
| Sprache | Englisch |
| Identifikator | ISBN: 978-3-7315-1053-6 KITopen-ID: 1000129216 |
| Erschienen in | Forum Bildverarbeitung 2020. Ed.: T. Längle ; M. Heizmann |
| Verlag | KIT Scientific Publishing |
| Bemerkung zur Veröffentlichung | Continuous quality monitoring is essential for automated production systems and efficient manufacturing. Laser welding processes are a key technology for many industrial applications and must fulfill high-quality requirements. Various influencing factors can lead to defects in the weld seam, which impair the functionality and quality of the end product. Therefore, a reliable quality assurance is a prerequisite for high product quality in welding processes. An indicator for an unstable situation in welding processes is the occurrence of spatter on the component. Thus, the detection of spatter can serve as a significant signal for defective weld seams. This article proposes the detection of spatter based on a camera image taken with an industrial camera, which is usually already integrated in the laser system. Due to the large variance of weld seams in image-based analysis, algorithms with a high degree of generalization are required. Using convolutional neural networks (CNN) and semantic segmentation the camera image is analyzed and classified pixel by pixel. The CNN is trained in a multi-class approach in order to recognize the weld seam as well as the spatter as result classes. The segmentation map constitutes the classification result. The results of the deep learning algorithms are evaluated by different methods and conclusions about their prediction quality are made. |
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