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    Verbesserungen beim Laserschneiden mit Methoden des maschinellen Lernens

    Dissertations in Series (Dissertationen in Schriftenreihe)

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    Author(s)
    Felica Tatzel, Leonie
    Collection
    AG Universitätsverlage
    Language
    German
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    Abstract
    Although laser cutting of metals is a well-established process, there is considerable potential for improvement with regard to various requirements for the manufacturing industry. First, this potential is identified and then it is shown how improvements could be made using machine learning. For this purpose, a database was generated. It contains the process parameters, RGB images, 3D point clouds and various quality features of almost 4000 cut edges.
    URI
    https://library.oapen.org/handle/20.500.12657/52956
    Keywords
    cut quality; convolutional neural network; machine learning; stainless steel; Laser cutting; Schnittqualität; Maschinelles Lernen; Edelstahl; Laserschneiden; Faltendes neuronales Netz
    DOI
    10.5445/KSP/1000137690
    ISBN
    9783731511281, 9783731511281
    Publisher
    KIT Scientific Publishing
    Publisher website
    https://www.ksp.kit.edu/index.php?link=shop&sort=all
    Publication date and place
    Karlsruhe, 2022
    Imprint
    KIT Scientific Publishing
    Series
    Forschungsberichte aus der Industriellen Informationstechnik, 24
    Classification
    Electrical engineering
    Pages
    234
    Rights
    http://creativecommons.org/licenses/by-sa/4.0
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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