IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i3d10.1007_s10845-023-02283-1.html
   My bibliography  Save this article

A digital twin-assisted deep transfer learning method towards intelligent thermal error modeling of electric spindles

Author

Listed:
  • Shuai Ma

    (Guangdong University of Technology)

  • Jiewu Leng

    (Guangdong University of Technology)

  • Pai Zheng

    (The Hong Kong Polytechnic University)

  • Zhuyun Chen

    (South China University of Technology)

  • Bo Li

    (Hubei University of Arts and Sciences
    XY-HUST Advanced Manufacturing Engineering Research Institute)

  • Weihua Li

    (South China University of Technology)

  • Qiang Liu

    (Guangdong University of Technology)

  • Xin Chen

    (Guangdong University of Technology)

Abstract

Thermal error modeling (TEM) is essential for preserving machining accuracy and enhancing the reliability of electric spindle systems. However, the major challenges in TEM lie in the limited or unavailable labeled thermal error samples due to the difficulties in data acquisition, as well as the problem of large distribution discrepancy between training and testing data under variable working conditions. Recently, digital twin (DT) has emerged as a promising tool in intelligent manufacturing. The DT model of the electric spindle can simulate system thermal behavior data that closely resembles real working conditions, providing a remarkable opportunity for TEM. Additionally, deep transfer learning (DTL) leverages existing knowledge to minimize data distribution discrepancies, bridging the gap between virtual and real data, and ultimately enhancing the generalization and adaptation ability of the model. Thus, this paper proposes a DT-assisted DTL method for TEM of electric spindles. Firstly, the DT model for the electric spindle is built by establishing a high-fidelity simulation model based on the physical system’s thermal behavior mechanism. Furthermore, temperature field information for all interested working conditions can be simulated from the constructed DT model. Subsequently, the distance-guided domain adversarial network (DGDAN) is developed, with data generated by the DT model constructed as the training data in the source domain, while partially collected data from the physical system is used as the target domain for training. To validate the effectiveness of the proposed method, a case study is conducted using datasets from both the DT model and the physical system. The experimental results demonstrate that the proposed method successfully achieves TEM in scenarios where the thermal error data is limited or unavailable from the physical system, and the goodness of fit is higher than the state-of-the-art methods by 11.73%.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:3:d:10.1007_s10845-023-02283-1
    DOI: 10.1007/s10845-023-02283-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-023-02283-1
    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/s10845-023-02283-1?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. 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.
    2. Zhenyu Liu & Donghao Zhang & Weiqiang Jia & Xianke Lin & Hui Liu, 2020. "An adversarial bidirectional serial–parallel LSTM-based QTD framework for product quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1511-1529, August.
    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. Zhangyue Shi & Abdullah Al Mamun & Chen Kan & Wenmeng Tian & Chenang Liu, 2023. "An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1815-1831, April.
    5. Sung-Mook Oh & Jin Park & Jinsun Yang & Young-Gyun Oh & Kyung-Woo Yi, 2023. "Smart classification method to detect irregular nozzle spray patterns inside carbon black reactor using ensemble transfer learning," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2729-2745, August.
    6. 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.
    7. Hakki Ozgur Unver & Batihan Sener, 2023. "A novel transfer learning framework for chatter detection using convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1105-1124, March.
    8. 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.
    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. 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.
    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. 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.
    4. 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.
    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. Hyunmin Park & Yun Seok Kang & Seung-Kyum Choi & Hyung Wook Park, 2025. "Quality evaluation modeling of a DED-processed metallic deposition based on ResNet-50 with few training data," Journal of Intelligent Manufacturing, Springer, vol. 36(4), pages 2677-2693, April.
    7. 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.
    8. Ágota Bányai, 2021. "Energy Consumption-Based Maintenance Policy Optimization," Energies, MDPI, vol. 14(18), pages 1-33, September.
    9. Wei Guo & Yijin Wang & Xin Chen & Pingyu Jiang, 2024. "Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application," Journal of Intelligent Manufacturing, Springer, vol. 35(4), pages 1439-1454, April.
    10. Hasan Tercan & Tobias Meisen, 2022. "Machine learning and deep learning based predictive quality in manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1879-1905, October.
    11. 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.
    12. Hongquan Gui & Jialan Liu & Chi Ma & Mengyuan Li, 2024. "Industrial-oriented machine learning big data framework for temporal-spatial error prediction and control with DTSMGCN model," Journal of Intelligent Manufacturing, Springer, vol. 35(3), pages 1173-1196, March.
    13. Ning Ge & Guanghao Li & Li Zhang & Yi Liu, 2022. "Failure prediction in production line based on federated learning: an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2277-2294, December.
    14. Junli Liu & Deyu Zhang & Zhongpeng Liu & Tianyu Guo & Yanyan Yan, 2024. "Construction of a Digital Twin System and Dynamic Scheduling Simulation Analysis of a Flexible Assembly Workshops with Island Layout," Sustainability, MDPI, vol. 16(20), pages 1-22, October.
    15. Yi Zhang & Peng Peng & Chongdang Liu & Yanyan Xu & Heming Zhang, 2022. "A sequential resampling approach for imbalanced batch process fault detection in semiconductor manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1057-1072, April.
    16. Dayuan Wu & Ping Yan & You Guo & Han Zhou & Jian Chen, 2022. "A gear machining error prediction method based on adaptive Gaussian mixture regression considering stochastic disturbance," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2321-2339, December.
    17. 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.
    18. Zhangyue Shi & Yuxuan Li & Chenang Liu, 2025. "Knowledge distillation-based information sharing for online process monitoring in decentralized manufacturing system," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 2177-2192, March.
    19. Leoni, Leonardo & De Carlo, Filippo & Tucci, Mario, 2023. "Developing a framework for generating production-dependent failure rate through discrete-event simulation," International Journal of Production Economics, Elsevier, vol. 266(C).
    20. 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.

    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:36:y:2025:i:3:d:10.1007_s10845-023-02283-1. 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.