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Model-Based Engineering of Multi-Purpose Digital Twins in Manufacturing

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Digital Twin

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Abstract

The engineering of digital twins for manufacturing can benefit greatly from automated or semi-automated methods, as opposed to the current manual software development methods limited to specific use cases. The manufacturing domain already holds a large amount of data and models that can be used in these engineering processes and during runtime of digital twins. Within this chapter, we show how to apply model-driven engineering methods to the development of digital twins. Specifically, we present the necessary architectural components required for the development of reusable digital twins supporting multiple purposes, as well as the data-to-model and model-to-model transformation methods that can be applied for digital twin engineering. In addition, we discuss the architecture and the runtime use of models in the digital twin, using relevant use cases from the manufacturing domain to illustrate our approach. Overall, we show that model-driven engineering approaches are transferable to engineering digital twins for manufacturing with all its advantages, such as mastering complexity and properly involving domain experts.

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Notes

  1. 1.

    We refer the readers to [17, 25] for a more detailed explanation of the concepts within the conceptual model of the digital shadow.

  2. 2.

    https://www.dagstuhl.de/de/seminars/seminar-calendar/seminar-details/22362.

  3. 3.

    https://www.digitaltwinconsortium.org/initiatives/the-definition-of-a-digital-twin/.

  4. 4.

    https://azure.microsoft.com/services/iot-hub/, https://azure.microsoft.com/services/digital-twins/, and https://azure.microsoft.com/services.

  5. 5.

    https://aws.amazon.com/de/greengrass/.

  6. 6.

    https://www.ibm.com/topics/what-is-a-digital-twin.

  7. 7.

    https://siemens.mindsphere.io/en.

  8. 8.

    https://www.eclipse.org/hono/, https://www.eclipse.org/vorto/, and https://www.eclipse.org/ditto/.

  9. 9.

    http://www.aka.ms/dtdl.

  10. 10.

    https://gemoc.org/events/moddit2023.

  11. 11.

    https://edt.community/.

  12. 12.

    https://www.automationml.org.

  13. 13.

    https://www.omg.org/spec/SysML.

  14. 14.

    https://freemarker.apache.org/.

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Acknowledgements

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2023 Internet of Production - 390621612. Website: https://www.iop.rwth-aachen.de. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - Model-Based DevOps - 505496753. Website: https://mbdo.github.io

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

    Malte Heithoff, Nico Jansen, Judith Michael, Florian Rademacher & Bernhard Rumpe

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Correspondence to Judith Michael .

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  1. School of Modeling, Simulation, and Training, University of Central Florida, Orlando, FL, USA

    Soheil Sabri

  2. OC Survey Geospatial Services, Orange County Public Works, Orange, CA, USA

    Kostas Alexandridis

  3. Research and Scholarships, Institute for Education, Los Angeles, CA, USA

    Newton Lee

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Heithoff, M., Jansen, N., Michael, J., Rademacher, F., Rumpe, B. (2024). Model-Based Engineering of Multi-Purpose Digital Twins in Manufacturing. In: Sabri, S., Alexandridis, K., Lee, N. (eds) Digital Twin. Springer, Cham. https://doi.org/10.1007/978-3-031-67778-6_5

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