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Foresighted digital twin for situational agent selection in production control

May, Marvin Carl; Overbeck, Leonard; Wurster, Marco; Kuhnle, Andreas; Lanza, Gisela


As intelligent Data Acquisition and Analysis in Manufacturing nears its apex, a new era of Digital Twins is dawning. Foresighted Digital Twins enable short- to medium-term system behavior predictions to infer optimal production operation strategies. Creating up-to-the-minute Digital Twins requires both the availability of real-time data and its incorporation and serve as a stepping-stone into developing unprecedented forms of production control. Consequently, we regard a new concept of Digital Twins that includes foresight, thereby enabling situational selection of production control agents. One critical element for adequate system predictions is human behavior as it is neither rule-based nor deterministic, which we therefore model applying Reinforcement Learning. Owing to these ever-changing circumstances, rigid operation strategies crucially restrain reactions, as opposed to circumstantial control strategies that hence can outperform traditional approaches. Building on enhanced foresights we show the superiority of this approach and present strategies for improved situational agent selection.

Verlagsausgabe §
DOI: 10.5445/IR/1000134700
Veröffentlicht am 04.07.2021
DOI: 10.1016/j.procir.2021.03.005
Zitationen: 7
Zitationen: 10
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2212-8271
KITopen-ID: 1000134700
Erschienen in Procedia CIRP
Verlag Elsevier
Band 99
Seiten 27–32
Nachgewiesen in Scopus
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