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dc.contributor.authorDornheim, Johannes
dc.date.accessioned2022-06-02T04:13:31Z
dc.date.available2022-06-02T04:13:31Z
dc.date.issued2022
dc.date.submitted2022-06-01T09:38:14Z
dc.identifierhttps://library.oapen.org/handle/20.500.12657/55801
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/82583
dc.description.abstractThe 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.
dc.languageGerman
dc.relation.ispartofseriesSchriftenreihe des Instituts für Angewandte Materialien, Karlsruher Institut für Technologie
dc.rightsopen access
dc.subject.otherMaschinelles Lernen; Entscheidungsoptimierung; Bestärkendes Lernen; Prozesspfadoptimierung; Optimale Regelung; machine learning; reinforcement learning; decision optimization; process optimization; optimal control
dc.titleModellfreies Lernen optimaler zeitdiskreter Regelungsstrategien für Fertigungsprozesse mit endlichem Zeithorizont
dc.typebook
oapen.identifier.doi10.5445/KSP/1000141283
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2
oapen.pages226
dc.seriesnumber96


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