Machine Learning for Camera-Based Monitoring of Laser Welding Processes
| dc.contributor.author | Hartung, Julia | |
| dc.date.accessioned | 2025-03-08T02:13:19Z | |
| dc.date.available | 2025-03-08T02:13:19Z | |
| dc.date.issued | 2024 | |
| dc.date.submitted | 2024-03-18T13:31:49Z | |
| dc.identifier | https://library.oapen.org/handle/20.500.12657/88624 | |
| dc.identifier.uri | https://doab-dev.siscern.org/handle/20.500.12854/175420 | |
| dc.description.abstract | The increasing use of automated laser welding processes causes high demands on process monitoring. This work demonstrates methods that use a camera mounted on the focussing optics to perform pre-, in-, and post-process monitoring of welding processes. The implementation uses machine learning methods. All algorithms consider the integration into industrial processes. These challenges include a small database, limited industrial manufacturing inference hardware, and user acceptance. | |
| dc.language | English | |
| dc.relation.ispartofseries | Forschungsberichte aus der Industriellen Informationstechnik | |
| dc.rights | open access | |
| dc.subject.other | CNN; stacked dilated U-Net; semantic segmentation; hairpin technology; laser welding; quality assurance; machine learning; Qualitätssicherung; semantische Segmentierung; Hairpin Technologie; Laserschweißen; Maschinelles Lernen; Künstliche Intelligenz | |
| dc.subject.other | thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering | |
| dc.title | Machine Learning for Camera-Based Monitoring of Laser Welding Processes | |
| dc.type | book | |
| oapen.identifier.doi | 10.5445/KSP/1000164716 | |
| oapen.relation.isPublishedBy | 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 | |
| oapen.pages | 258 | |
| dc.seriesnumber | 32 |
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