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Information fusion and systematic logic library-generation methods for self-configuration of autonomous digital twin

Author

Listed:
  • Kyu Tae Park

    (Sungkyunkwan University
    MICUBE Solution Inc.)

  • Sang Ho Lee

    (Sungkyunkwan University)

  • Sang Do Noh

    (Sungkyunkwan University)

Abstract

Self-configuration is the preparation required to facilitate smart-manufacturing (SM) with the inputs derived without user intervention for engineering applications. Thus, it is vital for achieving the highest maturity level of SM technologies. In context, digital twin (DT) is an advanced virtual factory with simulation as its core technical functionality. However, the requirement of several inputs limits the implementation of DT on a physical asset without user intervention. Moreover, surpassing this limitation requires extraction methods for deriving the necessary inputs for DT application. Therefore, this study proposes information fusion and systematic logic library (SLL)-generation methods to facilitate the self-configuration of an autonomous DT. The information fusion aggregates and extracts the information elements required for DT application from heterogeneous information sources. In addition, the SLL generation method created the SLL required for reflecting the functional units of agents within the physical asset. Both methods were proposed from available SM standards such as ISA-95, automation markup language, and OPC unified architecture. Furthermore, an autonomous DT-supporting framework was designed by analyzing the relationship between asset description and SM standards, which facilitated the artificial intelligence-based extraction of the asset description object and SLL. Additionally, the core functional engines within this framework were designed using machine learning and process-mining techniques. Consequently, the proposed methods reduced the input pre-processing time required for constructing and synchronizing an autonomous DT to aid the application of autonomous DT on the physical asset without user intervention.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:joinma:v:33:y:2022:i:8:d:10.1007_s10845-021-01795-y
    DOI: 10.1007/s10845-021-01795-y
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov, 2019. "The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics," International Journal of Production Research, Taylor & Francis Journals, vol. 57(3), pages 829-846, February.
    4. 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.
    5. 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.
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