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Sustainable Digital Twin Engineering for the Internet of Production

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Digital Twin Driven Intelligent Systems and Emerging Metaverse

Abstract

Digital twins becoming more prevalent: They are being used to support the design, operations, and analysis of complex systems in many domains, such as automotive, agriculture, avionics, construction, or medicine, and comprise much information about the systems and processes of the twinned system. Currently, digital twins are designed and engineered ad-hoc, in a piecemeal fashion. This hampers the research and application of digital twins. Based on our interdisciplinary research regarding the “Internet of Production”, we combine model-driven methods for the sustainable engineering of information systems, software architectures, and software language engineering to systematically engineer digital twins. Within this chapter, we discuss challenges on the road to a systematic engineering of digital twins, present our model-driven approach for the engineering of them as well as possible implementations. Our insights may guide researchers and practitioners to sustainable, planned, and efficient engineering and operations.

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Notes

  1. 1.

    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.

  2. 2.

    https://www.omg.org/spec/OCL/2.4/PDF.

  3. 3.

    http://mqtt.org.

  4. 4.

    https://digitaltwinexchange.ibm.com/.

  5. 5.

    https://docs.oracle.com/en/cloud/paas/iot-cloud/iotgs/oracle-iot-digital-twin-implementation.html.

  6. 6.

    https://siemens.mindsphere.io/content/dam/cloudcraze-mindsphere-assets/03-catalog-section/05-solution-packages/solution-packages/digitalize-and-transform/Siemens-MindSphere-Digitalize-and-Transform-sb-72224-A8.pdf.

  7. 7.

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

  8. 8.

    https://docs.microsoft.com/en-us/azure/digital-twins/overview.

  9. 9.

    https://github.com/eclipse/vorto.

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Author information

Authors and Affiliations

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

    Malte Heithoff, Judith Michael, Lukas Netz & Bernhard Rumpe

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

    Shan Fur, Jérôme Pfeiffer & Andreas Wortmann

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  1. Shan Fur
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  2. Malte Heithoff
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  3. Judith Michael
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  6. Bernhard Rumpe
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Correspondence to Judith Michael .

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Editors and Affiliations

  1. MSKU Metaverse Lab, Department of Computer Engineering, Faculty of Engineering, Mugla Sitki Kocman University, Mugla, Türkiye

    Enis Karaarslan

  2. Department of Electrical and Electronics Engineering, Faculty of Engineering, Manisa Celal Bayar University, Manisa, Türkiye

    Ömer Aydin

  3. Department of Electric Power Engineering, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology, Trondheim, Norway

    Ümit Cali

  4. Department of Computer Science, University of Antwerp and Flanders Make Strategic Research Center, Flanders, Belgium

    Moharram Challenger

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Fur, S. et al. (2023). Sustainable Digital Twin Engineering for the Internet of Production. In: Karaarslan, E., Aydin, Ö., Cali, Ü., Challenger, M. (eds) Digital Twin Driven Intelligent Systems and Emerging Metaverse. Springer, Singapore. https://doi.org/10.1007/978-981-99-0252-1_4

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