Machine Learning for Camera-Based Monitoring of Laser Welding Processes
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...
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| Format: | Online |
| Language: | English |
| Published: |
KIT Scientific Publishing
2024
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| Online Access: | OCN: 1427548475 |
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| _version_ | 1869515166475878400 |
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| author | Hartung, Julia |
| author_browse | Hartung, Julia |
| author_facet | Hartung, Julia |
| author_sort | Hartung, Julia |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-135794 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | KIT Scientific Publishing |
| publisherStr | KIT Scientific Publishing |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1357942025-05-27T07:04:57Z Machine Learning for Camera-Based Monitoring of Laser Welding Processes Hartung, Julia 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 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. 2024-03-19T04:06:27Z 2024-03-19T04:06:27Z 2024-03-18T13:31:49Z 2024 book OCN: 1427548475 https://library.oapen.org/handle/20.500.12657/88624 9783731513339 https://directory.doabooks.org/handle/20.500.12854/135794 eng Forschungsberichte aus der Industriellen Informationstechnik open access image/jpeg image/jpeg image/jpeg image/jpeg image/jpeg image/jpeg Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International Attribution-ShareAlike 4.0 International https://library.oapen.org/bitstream/20.500.12657/88624/1/machine-learning-for-camera-based-monitoring-of-laser-welding-processes.pdf https://library.oapen.org/bitstream/20.500.12657/88624/1/machine-learning-for-camera-based-monitoring-of-laser-welding-processes.pdf https://library.oapen.org/bitstream/20.500.12657/88624/1/machine-learning-for-camera-based-monitoring-of-laser-welding-processes.pdf https://library.oapen.org/bitstream/20.500.12657/88624/1/machine-learning-for-camera-based-monitoring-of-laser-welding-processes.pdf https://library.oapen.org/bitstream/20.500.12657/88624/1/machine-learning-for-camera-based-monitoring-of-laser-welding-processes.pdf https://library.oapen.org/bitstream/20.500.12657/88624/1/machine-learning-for-camera-based-monitoring-of-laser-welding-processes.pdf KIT Scientific Publishing 10.5445/KSP/1000164716 10.5445/KSP/1000164716 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2 9783731513339 AG Universitätsverlage 258 open access |
| spellingShingle | 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 Hartung, Julia Machine Learning for Camera-Based Monitoring of Laser Welding Processes |
| title | Machine Learning for Camera-Based Monitoring of Laser Welding Processes |
| title_full | Machine Learning for Camera-Based Monitoring of Laser Welding Processes |
| title_fullStr | Machine Learning for Camera-Based Monitoring of Laser Welding Processes |
| title_full_unstemmed | Machine Learning for Camera-Based Monitoring of Laser Welding Processes |
| title_short | Machine Learning for Camera-Based Monitoring of Laser Welding Processes |
| title_sort | machine learning for camera based monitoring of laser welding processes |
| topic | 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 |
| topic_facet | 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 |
| url | OCN: 1427548475 |
| work_keys_str_mv | AT hartungjulia machinelearningforcamerabasedmonitoringoflaserweldingprocesses |