IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v35y2024i6d10.1007_s10845-023-02172-7.html
   My bibliography  Save this article

Digital Twin-based manufacturing system: a survey based on a novel reference model

Author

Listed:
  • Shimin Liu

    (Donghua University
    The Hong Kong Polytechnic University
    The Hong Kong Polytechnic University)

  • Pai Zheng

    (The Hong Kong Polytechnic University
    The Hong Kong Polytechnic University)

  • Jinsong Bao

    (Donghua University)

Abstract

The development of advanced information technologies are paving the digital transformation of manufacturing systems, of which Digital Twin-based manufacturing system (DTMS) has become a prevailing topic attracted ever-increasing concerns from both industry and academia. As a cutting-edge smart manufacturing system, DTMS can improve manufacturing accuracy and efficiency based on high fidelity simulation, near real-time monitoring and control in a cyber-physical integrated manner. However, the connotation and boundary of DTMS lack a clear definition and systematic analysis. Therefore, this paper reviews the existing Digital Twin reference models and implementation architectures on manufacturing system, and further proposes a reference model of DTMS. Based on it, the characteristics and operational mechanism of DTMS are analyzed from three perspectives: hierarchy, dimension, and scale. Specifically, the composition of DTMS is described from a multi-level perspective, the specific characteristics of the DTMS are analyzed from a multi-dimensional perspective, and the temporal and spatial characteristics of the DTMS under different application scenarios are depicted from a multi-scale perspective, respectively. At last, the potential research directions of DTMS are highlighted in terms of reusability, interpretability and adaptability. It is envisioned that this work can provide a clear understanding with insightful knowledge to attract more in-depth research of DTMS.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:joinma:v:35:y:2024:i:6:d:10.1007_s10845-023-02172-7
    DOI: 10.1007/s10845-023-02172-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-023-02172-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10845-023-02172-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
    2. Jyrki Savolainen & Michele Urbani, 2021. "Maintenance optimization for a multi-unit system with digital twin simulation," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1953-1973, October.
    3. Guanghui Zhou & Chao Zhang & Zhi Li & Kai Ding & Chuang Wang, 2020. "Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(4), pages 1034-1051, February.
    4. Jinjiang Wang & Lunkuan Ye & Robert X. Gao & Chen Li & Laibin Zhang, 2019. "Digital Twin for rotating machinery fault diagnosis in smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3920-3934, June.
    5. 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.
    6. Xin Tong & Qiang Liu & Shiwei Pi & Yao Xiao, 2020. "Real-time machining data application and service based on IMT digital twin," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1113-1132, June.
    7. Elisa Negri & Vibhor Pandhare & Laura Cattaneo & Jaskaran Singh & Marco Macchi & Jay Lee, 2021. "Field-synchronized Digital Twin framework for production scheduling with uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1207-1228, April.
    8. Maurizio Bevilacqua & Eleonora Bottani & Filippo Emanuele Ciarapica & Francesco Costantino & Luciano Di Donato & Alessandra Ferraro & Giovanni Mazzuto & Andrea Monteriù & Giorgia Nardini & Marco Orten, 2020. "Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants," Sustainability, MDPI, vol. 12(3), pages 1-17, February.
    9. Aniruddha Gaikwad & Reza Yavari & Mohammad Montazeri & Kevin Cole & Linkan Bian & Prahalada Rao, 2020. "Toward the digital twin of additive manufacturing: Integrating thermal simulations, sensing, and analytics to detect process faults," IISE Transactions, Taylor & Francis Journals, vol. 52(11), pages 1204-1217, November.
    10. 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.
    11. A. J. H. Redelinghuys & A. H. Basson & K. Kruger, 2020. "A six-layer architecture for the digital twin: a manufacturing case study implementation," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1383-1402, August.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nguyen, Tiep & Duong, Quang Huy & Nguyen, Truong Van & Zhu, You & Zhou, Li, 2022. "Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 244(C).
    2. 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).
    3. Georgios Falekas & Athanasios Karlis, 2021. "Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects," Energies, MDPI, vol. 14(18), pages 1-26, September.
    4. Kamble, Sachin S & Gunasekaran, Angappa & Parekh, Harsh & Mani, Venkatesh & Belhadi, Amine & Sharma, Rohit, 2022. "Digital twin for sustainable manufacturing supply chains: Current trends, future perspectives, and an implementation framework," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    5. 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.
    6. 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.
    7. Saporiti, Nicolò & Cannas, Violetta Giada & Pozzi, Rossella & Rossi, Tommaso, 2023. "Challenges and countermeasures for digital twin implementation in manufacturing plants: A Delphi study," International Journal of Production Economics, Elsevier, vol. 261(C).
    8. 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.
    9. Teng, Sin Yong & Touš, Michal & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav, 2021. "Recent advances on industrial data-driven energy savings: Digital twins and infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    10. 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.
    11. Jielin Chen & Shuang Li & Hanwei Teng & Xiaolong Leng & Changping Li & Rendi Kurniawan & Tae Jo Ko, 2025. "Digital twin-driven real-time suppression of delamination damage in CFRP drilling," Journal of Intelligent Manufacturing, Springer, vol. 36(2), pages 1459-1476, February.
    12. 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.
    13. Ahmed Ktari & Mohamed El Mansori, 2022. "Digital twin of functional gating system in 3D printed molds for sand casting using a neural network," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 897-909, March.
    14. Jyrki Savolainen & Michele Urbani, 2021. "Maintenance optimization for a multi-unit system with digital twin simulation," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1953-1973, October.
    15. 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.
    16. Seon Han Choi & Byeong Soo Kim, 2025. "Intelligent factory layout design framework through collaboration between optimization, simulation, and digital twin," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 1547-1561, March.
    17. Weifei Hu & Jinyi Shao & Qing Jiao & Chuxuan Wang & Jin Cheng & Zhenyu Liu & Jianrong Tan, 2023. "A new differentiable architecture search method for optimizing convolutional neural networks in the digital twin of intelligent robotic grasping," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2943-2961, October.
    18. Rong Xie & Muyan Chen & Weihuang Liu & Hongfei Jian & Yanjun Shi, 2021. "Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review," Sustainability, MDPI, vol. 13(5), pages 1-22, February.
    19. PengYu Wang & Wen-An Yang & YouPeng You, 2023. "A cyber-physical prototype system in augmented reality using RGB-D camera for CNC machining simulation," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3637-3658, December.
    20. Lim, Kendrik Yan Hong & Dang, Le Van & Chen, Chun-Hsien, 2024. "Incorporating supply and production digital twins to mitigate demand disruptions in multi-echelon networks," International Journal of Production Economics, Elsevier, vol. 273(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:35:y:2024:i:6:d:10.1007_s10845-023-02172-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.