IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i2d10.1007_s10845-023-02315-w.html
   My bibliography  Save this article

Digital twin-driven real-time suppression of delamination damage in CFRP drilling

Author

Listed:
  • Jielin Chen

    (Yeungnam University)

  • Shuang Li

    (Yeungnam University)

  • Hanwei Teng

    (Yeungnam University)

  • Xiaolong Leng

    (Yeungnam University)

  • Changping Li

    (Hunan University of Science and Technology)

  • Rendi Kurniawan

    (Yeungnam University)

  • Tae Jo Ko

    (Yeungnam University)

Abstract

Delamination damage should be avoided because it severely affects the quality of CFRP products. This paper proposes a digital twin (DT) driven method for real-time suppression of delamination damage to ensure the highest quality hole exit. The relationship between the increase in thrust caused by tool wear and CFRP delamination was analyzed through extensive drilling experiments. The evolving twin models were developed to integrate the virtual space of the drilling process. Once the cutting parameters and thrust signals were input into the twin, the Gaussian process regression and mathematical models predicted the current tool wear and thrust curve, respectively. The feedback results from the DT dynamically interact with the real drilling operation after the optimization function solves the current critical feed rate (CFR). A DT scheme was designed, and the performance of the deployed DT was tested through an online service panel. The results show that the DT has excellent real-time prediction capability within 100 hole-making cycles, with maximum errors of 4.1% and 4.2% for tool wear and thrust prediction at the exit, respectively. Compared to conventional drilling (CD), DT technology provides closed-loop feedback on the time-varying CFR for each hole, resulting in no delamination mode I and up to 48.4% suppression of delamination mode III. This research has achieved intelligent virtual-real linkage in the CFRP drilling process, providing important theoretical support for effectively suppressing delamination damage in the automated production process.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:joinma:v:36:y:2025:i:2:d:10.1007_s10845-023-02315-w
    DOI: 10.1007/s10845-023-02315-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-023-02315-w
    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/s10845-023-02315-w?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. 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.
    3. Fei Tao & Qinglin Qi, 2019. "Make more digital twins," Nature, Nature, vol. 573(7775), pages 490-491, September.
    4. Zengya Zhao & Sibao Wang & Zehua Wang & Shilong Wang & Chi Ma & Bo Yang, 2022. "Surface roughness stabilization method based on digital twin-driven machining parameters self-adaption adjustment: a case study in five-axis machining," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 943-952, April.
    5. 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.
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    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. 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.
    9. 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.
    10. Xin Tong & Qiang Liu & Yinuo Zhou & Pengpeng Sun, 2025. "A digital twin-driven cutting force adaptive control approach for milling process," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 551-568, January.
    11. Zhicheng Xu & Vignesh Selvaraj & Sangkee Min, 2025. "Intelligent G-code-based power prediction of ultra-precision CNC machine tools through 1DCNN-LSTM-Attention model," Journal of Intelligent Manufacturing, Springer, vol. 36(2), pages 1237-1260, 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. 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.
    14. Maksim Dli & Andrei Puchkov & Valery Meshalkin & Ildar Abdeev & Rail Saitov & Rinat Abdeev, 2020. "Energy and Resource Efficiency in Apatite-Nepheline Ore Waste Processing Using the Digital Twin Approach," Energies, MDPI, vol. 13(21), pages 1-13, November.
    15. Jian-Guo Duan & Tian-Yu Ma & Qing-Lei Zhang & Zhen Liu & Ji-Yun Qin, 2023. "Design and application of digital twin system for the blade-rotor test rig," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 753-769, February.
    16. Aniket Nagargoje & Pavan Kumar Kankar & Prashant Kumar Jain & Puneet Tandon, 2023. "Application of artificial intelligence techniques in incremental forming: a state-of-the-art review," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 985-1002, March.
    17. Xinzhou Wu & Zhe Cheng & Victor E. Kuzmichev, 2023. "Dynamic Fit Optimization and Effect Evaluation of a Female Wetsuit Based on Virtual Technology," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    18. Cosmin Aron & Fabio Sgarbossa & Eric Ballot & Dmitry Ivanov, 2024. "Cloud material handling systems: a cyber-physical system to enable dynamic resource allocation and digital interoperability," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 3815-3836, December.
    19. Sajjad Rahmanzadeh & Mir Saman Pishvaee & Kannan Govindan, 2023. "Emergence of open supply chain management: the role of open innovation in the future smart industry using digital twin network," Annals of Operations Research, Springer, vol. 329(1), pages 979-1007, October.
    20. Zander, Bennet & Lange, Kerstin & Haasis, Hans-Dietrich, 2021. "Designing the data supply chain of a smart construction factory," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 41-62, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.

    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:2:d:10.1007_s10845-023-02315-w. 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.