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Framework for automatic production simulation tuning with machine learning

May, Marvin Carl ORCID iD icon 1; Finke, Alexander 1; Theuner, Katharina 1; Lanza, Gisela 1
1 Institut für Produktionstechnik (WBK), Karlsruher Institut für Technologie (KIT)

Abstract:

Production system simulation is a powerful tool for optimizing the use of resources on both the planning and control level. However, creating and tuning such models manually is a tedious and error-prone task. Despite some approaches to automate this process, the state-of-the-art relies on the generation of models, by incorporating the knowledge of experts. Nevertheless, effectively creating and tuning such production simulations is, thus, a key driver for reducing costs, carbon footprint, and tardiness and therefore an essential factor in today´s production. Beneficial would be automated and flexible frameworks, since these are applicable to different use cases requiring less effort. Yet, in the age of Industry 4.0, data is ubiquitous and easily available and can serve as a basis for virtual models representing reality. Increasingly, these virtual models shall be interlinked with the current state of real-world systems to form so-called digital twins. As automated and flexible frameworks are missing, this paper proposes a novel approach where observed real system behavior is used and fed into a large-scale machine learning model trained on a plethora of possible parameter sets. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000169033
Veröffentlicht am 05.03.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2024
Sprache Englisch
Identifikator ISSN: 2212-8271
KITopen-ID: 1000169033
Erschienen in Procedia CIRP
Verlag Elsevier
Band 121
Seiten 49 – 54
Bemerkung zur Veröffentlichung Part of special issue: 11th CIRP Global Web Conference (CIRPe 2023)
Schlagwörter Production Simulation, Machine Learning, Simulation Tuning, Simulation Generation
Nachgewiesen in Dimensions
Scopus
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