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VREDI: virtual representation for a digital twin application in a work-center-level asset administration shell

Author

Listed:
  • Kyu Tae Park

    (Sungkyunkwan University
    Digital Factory Solution Center, MICUBE Solution Inc.)

  • Jinho Yang

    (Sungkyunkwan University)

  • Sang Do Noh

    (Sungkyunkwan University)

Abstract

The asset administration shell (AAS) has a virtual representation as an asset description and technical functionality as a smart manufacturing service. A digital twin (DT) is an advanced virtual factory technology that has simulation as its core technical functionality, which it performs in the type and instance stages of the physical asset. For providing an efficient information object to the DT application, this paper proposes Virtual REpresentation for a DIgital twin application (VREDI): an asset description for the operation procedures of a work-center-level DT application. For the successful application of DT as a smart factory technology, VREDI is designed to meet four core technical requirements—DT definition, AAS property inheritance, improving the existing asset description, and supporting DT-based technical functionalities. Based on the analysis of the technical requirements, the elements of VREDI are derived and the reference relationships between them are designed. It is then possible to provide the required technical functionality using the VREDI header, and a detailed P4R structure and elements of the body are defined. VREDI is applied to the concept to support the main properties of the DT. It is designed to inherit the AAS properties for efficient information management and interoperability. The application of advanced concepts such as “type and instance” and supporting vertical integration and horizontal coordination overcomes the limitations of the existing asset descriptions. Additionally, VREDI designates elements for supporting six DT-based technical functionalities in the type and instance stages of the physical work center.

Suggested Citation

  • Kyu Tae Park & Jinho Yang & Sang Do Noh, 2021. "VREDI: virtual representation for a digital twin application in a work-center-level asset administration shell," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 501-544, February.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:2:d:10.1007_s10845-020-01586-x
    DOI: 10.1007/s10845-020-01586-x
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    References listed on IDEAS

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    1. D.-Y. Kim & J.-W. Park & S. Baek & K.-B. Park & H.-R. Kim & J.-I. Park & H.-S. Kim & B.-B. Kim & H.-Y. Oh & K. Namgung & W. Baek, 2020. "A modular factory testbed for the rapid reconfiguration of manufacturing systems," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 661-680, March.
    2. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
    3. Qiang Liu & Hao Zhang & Jiewu Leng & Xin Chen, 2019. "Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3903-3919, June.
    4. Byeongwoo Jeon & Joo-Sung Yoon & Jumyung Um & Suk-Hwan Suh, 2020. "The architecture development of Industry 4.0 compliant smart machine tool system (SMTS)," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1837-1859, December.
    5. Juan Pablo Usuga Cadavid & Samir Lamouri & Bernard Grabot & Robert Pellerin & Arnaud Fortin, 2020. "Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1531-1558, August.
    6. Ngai, E.W.T. & Moon, Karen K.L. & Riggins, Frederick J. & Yi, Candace Y., 2008. "RFID research: An academic literature review (1995-2005) and future research directions," International Journal of Production Economics, Elsevier, vol. 112(2), pages 510-520, April.
    7. SooCheol Yoon & Jumyung Um & Suk-Hwan Suh & Ian Stroud & Joo-Sung Yoon, 2019. "Smart Factory Information Service Bus (SIBUS) for manufacturing application: requirement, architecture and implementation," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 363-382, January.
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    Cited by:

    1. Kyu Tae Park & Sang Ho Lee & Sang Do Noh, 2022. "Information fusion and systematic logic library-generation methods for self-configuration of autonomous digital twin," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2409-2439, December.

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