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    • Machine Learning for Camera-Based Monitoring of Laser Welding Processes

    Julia Hartung

    Band 32 von Forschungsberichte aus der Industriellen Informationstechnik
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    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.

    Umfang: XVI, 226 S.

    Preis: 46.00 €

    Fächer:

    Elektrotechnik 

    Schlagwörter:

    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

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    Empfohlene Zitierweise
    Hartung, J. 2024. Machine Learning for Camera-Based Monitoring of Laser Welding Processes. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000164716
    Hartung, J., 2024. Machine Learning for Camera-Based Monitoring of Laser Welding Processes. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000164716
    Hartung, J. Machine Learning for Camera-based Monitoring of Laser Welding Processes. KIT Scientific Publishing, 2024. DOI: https://doi.org/10.5445/KSP/1000164716
    Hartung, J. (2024). Machine Learning for Camera-Based Monitoring of Laser Welding Processes. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000164716
    Hartung, Julia. 2024. Machine Learning for Camera-based Monitoring of Laser Welding Processes. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000164716



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    Veröffentlicht am 8. März 2024

    Sprache

    Englisch

    Seitenanzahl:

    258

    DOI
    https://doi.org/10.5445/KSP/1000164716