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    Modellfreies Lernen optimaler zeitdiskreter Regelungsstrategien für Fertigungsprozesse mit endlichem Zeithorizont

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    Author(s)
    Dornheim, Johannes cc
    Language
    German
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    Abstract
    The quality and performance of components is largely determined by the execution of the manufacturing processes involved. The process result depends -- in addition to the initial state of the component and the process -- on the course of the process. In many manufacturing processes, the course of the process can be decisively determined by manipulated variables that change over time. This work deals with methods to optimize these time-varying quantities under fluctuating process conditions. The quality of components depend to a large extent on the execution of the industrial processes involved in manufacturing. In addition to the initial conditions of the component and the process, the process result depends on the course of the process, which often can be significantly determined by time-varying manipulated variables. Methods for the optimization of these time-dependent quantities with regard to the component quality and depending on process conditions is the subject of this work.
    URI
    https://library.oapen.org/handle/20.500.12657/55801
    Keywords
    Maschinelles Lernen; Entscheidungsoptimierung; Bestärkendes Lernen; Prozesspfadoptimierung; Optimale Regelung; machine learning; reinforcement learning; decision optimization; process optimization; optimal control
    DOI
    10.5445/KSP/1000141283
    ISBN
    9783731511588
    Publisher
    KIT Scientific Publishing
    Publisher website
    https://www.ksp.kit.edu/index.php?link=shop&sort=all
    Publication date and place
    2022
    Series
    Schriftenreihe des Instituts für Angewandte Materialien, Karlsruher Institut für Technologie, 96
    Classification
    Mechanical engineering & materials
    Pages
    226
    Rights
    https://creativecommons.org/licenses/by/4.0/
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    Credits

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    • logo EU
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    • 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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