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dc.contributor.authorHartung, Julia
dc.date.accessioned2025-03-08T02:13:19Z
dc.date.available2025-03-08T02:13:19Z
dc.date.issued2024
dc.date.submitted2024-03-18T13:31:49Z
dc.identifierhttps://library.oapen.org/handle/20.500.12657/88624
dc.identifier.urihttps://doab-dev.siscern.org/handle/20.500.12854/175420
dc.description.abstractThe 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.languageEnglish
dc.relation.ispartofseriesForschungsberichte aus der Industriellen Informationstechnik
dc.rightsopen access
dc.subject.otherCNN; 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.otherthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering
dc.titleMachine Learning for Camera-Based Monitoring of Laser Welding Processes
dc.typebook
oapen.identifier.doi10.5445/KSP/1000164716
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2
oapen.pages258
dc.seriesnumber32


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