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IFIP International Conference on Advances in Production Management Systems

APMS 2021: Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems pp 333–341Cite as

Towards Development Platforms for Digital Twins: A Model-Driven Low-Code Approach

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Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 630))

Abstract

Digital Twins in smart manufacturing must be highly adaptable for different challenges, environments, and system states. In practice, there is a need for enabling the configuration of Digital Twins by domain experts. Low-code approaches seem to be a meaningful solution for configuration purposes but often lack extension options. We propose a model-driven low-code approach for the configuration and reconfiguration of Digital Twins using language plugins. This approach uses model-driven software engineering and software language engineering methods to derive a configurable digital twin implementation. Moreover, we discuss some remaining challenges such as interoperability, language modularity, evolution, integration of assistive services, collaborative development, and web-based debugging.

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Author information

Authors and Affiliations

  1. Software Engineering, RWTH Aachen University, Aachen, Germany

    Judith Michael

  2. Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW), University of Stuttgart, Stuttgart, Germany

    Andreas Wortmann

Authors
  1. Judith Michael
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  2. Andreas Wortmann
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Correspondence to Judith Michael .

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Editors and Affiliations

  1. IMT Atlantique, Nantes, France

    Alexandre Dolgui

  2. Centrale Nantes, Nantes, France

    Alain Bernard

  3. IMT Atlantique, Nantes, France

    David Lemoine

  4. ZF Friedrichshafen AG, Friedrichshafen, Germany

    Gregor von Cieminski

  5. Tecnológico de Monterrey, Mexico City, Mexico

    David Romero

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Michael, J., Wortmann, A. (2021). Towards Development Platforms for Digital Twins: A Model-Driven Low-Code Approach. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 630. Springer, Cham. https://doi.org/10.1007/978-3-030-85874-2_35

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  • DOI: https://doi.org/10.1007/978-3-030-85874-2_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85873-5

  • Online ISBN: 978-3-030-85874-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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