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            Machine Learning for Camera-Based Monitoring of Laser Welding Processes

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            Author(s)
            Hartung, Julia
            Language
            English
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            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.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/175420
            Keywords
            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; thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering
            DOI
            10.5445/KSP/1000164716
            Publisher
            KIT Scientific Publishing
            Publisher website
            http://www.ksp.kit.edu/
            Publication date and place
            2024
            Series
            Forschungsberichte aus der Industriellen Informationstechnik,
            Pages
            258
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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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