Marco Wurster, M.Sc.

  • 76131 Karlsruhe
    Kaiserstraße 12

Marco Wurster, M.Sc.

Forschungs- und Arbeitsgebiete:

  • Maschinelles Lernen in der Produktionsplanung und -steuerung
  • Skalierbare Automatisierung agiler Produktionssysteme
  • Plug&Produce
  • Remanufacturing auf Fabrikebene
  • Fabrikplanung

Allgemeine Aufgaben:

  • Vorlesungsbetreuer der Vorlesung Integrierte Produktionsplanung im Zeitalter von I4.0 (IPP)
    • Montageplanung: Konzept- und Detailplanung (Vorlesung und Übung)
  • Koordinator des Lernfabrik Moduls „Skalierbare Automatisierung“
  • Vertreter im Arbeitskreis Industrie 4.0 der IHK Karlsruhe

Projekte:

Versuchsstände:

Lebenslauf:

seit 10/2019 Wissenschaftlicher Mitarbeiter am Institut für Produktionstechnik (wbk) des Karlsruher Instituts für Technologie (KIT) 
10/2012-06/2019  Studium des Maschinenbaus am Karlsruher Institut für Technologie (KIT)
09/04/1993 Geboren in Breisach am Rhein

 

Veröffentlichungen

[ 1 ] May, M. C.; Overbeck, L.; Wurster, M.; Kuhnle, A. & Lanza, G. (2020), „Foresighted Digital Twin for situational Agent Selection in Production Control“. Elsevier, S. 27-32. 10.1016/j.procir.2021.03.005
Abstract
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.

[ 2 ] Wurster, M.; Häfner, B.; Gauder, D.; Stricker, N. & Lanza, G. (2021), „Fluid Automation - A Definition and an Application in Remanufacturing Production Systems“. Digitalizing smart factories, Elsevier, S. 508-513. 10.1016/j.procir.2020.05.267
Abstract
Production systems must be able to quickly adapt to changing requirements. Especially in the field of remanufacturing, the uncertainty in the state of the incoming products is very high. Several adaptation mechanisms can be applied leading to agile and changeable production systems. Among these, adapting the degree of automation with respect to changeover times and high investment costs is one of the most challenging mechanisms. However, not only long-term changes, but also short-term adaptations can lead to enormous potentials, e.g. when night shifts can be supported by robots and thus higher labor costs and unfavorable working conditions at night can be avoided. These changes in the degree of automation on an operational level are referred to as fluid automation, which will be defined in this paper. The mechanisms of fluid automation are presented together with a case study showing its application on a disassembly station for electrical drives.