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A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach

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  • Konstantinos Mykoniatis

    (Auburn University)

  • Gregory A. Harris

    (Auburn University)

Abstract

Virtual commissioning is a key technology in Industry 4.0 that can address issues faced by engineers during early design phases. The process of virtual commissioning involves the creation of a Digital Twin—a dynamic, virtual representation of a corresponding physical system. The digital twin model can be used for testing and verifying the control system in a simulated virtual environment to achieve rapid set-up and optimization prior to physical commissioning. Additionally, the modular production control systems, can be integrated and tested during or prior to the construction of the physical system. This paper describes the implementation of a digital twin emulator of an automated mechatronic modular production system that is linked with the running programmable logic controllers and allow for exchanging near real-time information with the physical system. The development and deployment of the digital twin emulator involves a novel hybrid simulation- and data-driven modeling approach that combines Discrete Event Simulation and Agent Based Modeling paradigms. The Digital Twin Emulator can support design decisions, test what-if system configurations, verify and validate the actual behavior of the complete system off-line, test realistic reactions, and provide statistics on the system’s performance.

Suggested Citation

  • Konstantinos Mykoniatis & Gregory A. Harris, 2021. "A digital twin emulator of a modular production system using a data-driven hybrid modeling and simulation approach," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1899-1911, October.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:7:d:10.1007_s10845-020-01724-5
    DOI: 10.1007/s10845-020-01724-5
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    References listed on IDEAS

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    1. Hans-Michael Hanisch & Andrei Lobov & Jose L. Martinez Lastra & Reijo Tuokko & Valeriy Vyatkin, 2006. "Formal validation of intelligent-automated production systems: towards industrial applications," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 8(1/2/3), pages 75-106.
    2. Fei Tao & Fangyuan Sui & Ang Liu & Qinglin Qi & Meng Zhang & Boyang Song & Zirong Guo & Stephen C.-Y. Lu & A. Y. C. Nee, 2019. "Digital twin-driven product design framework," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3935-3953, June.
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    Cited by:

    1. Shuai Ma & Jiewu Leng & Pai Zheng & Zhuyun Chen & Bo Li & Weihua Li & Qiang Liu & Xin Chen, 2025. "A digital twin-assisted deep transfer learning method towards intelligent thermal error modeling of electric spindles," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 1659-1688, March.
    2. Kunyu Wang & Lin Zhang & Zidi Jia & Hongbo Cheng & Han Lu & Jin Cui, 2024. "A framework and method for equipment digital twin dynamic evolution based on IExATCN," Journal of Intelligent Manufacturing, Springer, vol. 35(4), pages 1571-1583, April.
    3. Chi Ma & Hongquan Gui & Jialan Liu, 2023. "Self learning-empowered thermal error control method of precision machine tools based on digital twin," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 695-717, February.
    4. Neto, Anis Assad & Ribeiro da Silva, Elias & Deschamps, Fernando & do Nascimento Junior, Laercio Alves & Pinheiro de Lima, Edson, 2023. "Modeling production disorder: Procedures for digital twins of flexibility-driven manufacturing systems," International Journal of Production Economics, Elsevier, vol. 260(C).
    5. Yuchen Wang & Xinheng Wang & Ang Liu & Junqing Zhang & Jinhua Zhang, 2025. "Ontology of 3D virtual modeling in digital twin: a review, analysis and thinking," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 95-145, January.
    6. Ayman AboElHassan & Soumaya Yacout, 2023. "A digital shadow framework using distributed system concepts," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3579-3598, December.
    7. Jiewu Leng & Zisheng Lin & Zhiqiang Huang & Ruijun Ye & Qiang Liu & Xin Chen, 2024. "Rapid simplification of 3D geometry model of mechanisms in the digital twins-driven manufacturing system design," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2765-2786, August.
    8. Shimin Liu & Pai Zheng & Jinsong Bao, 2024. "Digital Twin-based manufacturing system: a survey based on a novel reference model," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2517-2546, August.
    9. Benno Gerlach & Simon Zarnitz & Benjamin Nitsche & Frank Straube, 2021. "Digital Supply Chain Twins—Conceptual Clarification, Use Cases and Benefits," Logistics, MDPI, vol. 5(4), pages 1-24, December.
    10. Guo Zhou & Kai Zhou & Jing Zhang & Meng Yuan & Xiaohao Wang & Pingfa Feng & Min Zhang & Feng Feng, 2024. "Digital modeling-driven chatter suppression for thin-walled part manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 289-305, January.
    11. Tangbin Xia & He Sun & Yutong Ding & Dongyang Han & Wei Qin & Joachim Seidelmann & Lifeng Xi, 2025. "Digital twin-based real-time energy optimization method for production line considering fault disturbances," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 569-593, January.
    12. Xiangdong Wang & Xiaofeng Hu & Jiafu Wan, 2024. "Digital-twin based real-time resource allocation for hull parts picking and processing," Journal of Intelligent Manufacturing, Springer, vol. 35(2), pages 613-632, February.

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