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        Modellfreies Lernen optimaler zeitdiskreter Regelungsstrategien für Fertigungsprozesse mit endlichem Zeithorizont

        Dissertations in Series (Dissertationen in Schriftenreihe)

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        Author(s)
        Dornheim, Johannes
        Collection
        AG Universitätsverlage
        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, 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
        Pages
        226
        Rights
        https://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        • 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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