IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i3d10.1007_s10845-024-02353-y.html
   My bibliography  Save this article

In-process surface quality monitoring of the slender workpiece machining with digital twin approach

Author

Listed:
  • Kaibo Lu

    (Taiyuan University of Technology)

  • Zhen Li

    (Taiyuan University of Technology)

  • Andrew Longstaff

    (University of Huddersfield)

Abstract

In-process monitoring of production quality plays a significant role in intelligent manufacturing. Both part deformation and vibration happen simultaneously in machining processes. They are two prominent issues that can affect the surface quality of machined parts, especially those with low rigidity. The purpose of this study is to explore a hybrid modeling solution to simultaneously monitor the diametrical errors and early chatter vibrations when turning a slender workpiece. A generic analytical model of the slender workpiece turning is formulated based on dynamics of machining. It is proved that the system complies with the principle of superposition. Accordingly, the explicit expressions free from cutting force modeling that is widely used in literature are derived for characterizing the finished surface quality with analytical and finite element methods, respectively. A data-driven model is also developed using the wavelet packet transform to the displacement signals. The independent decompositions of the displacement signals are then correlated with both the dimensional error model and the turning chatter model. Interconnecting the dynamics-based model and the data-driven model contributes to a digital-twin prototype, which allows for in-process detection of the geometrical distortion and the onset of chatter on the part surface. Finally, two different machining cases were performed to verify the proposed methodology. The results show that the developed model consisting of the formulated deflection correlation and chatter indicator is capable of simultaneously evaluating and detecting the dimensional error prediction and the early-onset chatter. By comparison with the analytical modeling, the high fidelity digital twin using the finite element modeling could exhibit higher prediction accuracy. The proposed monitoring strategy could provide a pragmatic approach to online quality control for intelligent machining of flexible workpieces on the shop floor.

Suggested Citation

  • Kaibo Lu & Zhen Li & Andrew Longstaff, 2025. "In-process surface quality monitoring of the slender workpiece machining with digital twin approach," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 2039-2053, March.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:3:d:10.1007_s10845-024-02353-y
    DOI: 10.1007/s10845-024-02353-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-024-02353-y
    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/s10845-024-02353-y?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. Baifan Zhou & Tim Pychynski & Markus Reischl & Evgeny Kharlamov & Ralf Mikut, 2022. "Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 1139-1163, April.
    2. 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.
    3. Noel P. Greis & Monica L. Nogueira & Sambit Bhattacharya & Catherine Spooner & Tony Schmitz, 2023. "Stability modeling for chatter avoidance in self-aware machining: an application of physics-guided machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 387-413, January.
    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. Jie Zhou & Zerui Xi & Shilong Wang & Bo Yang & Youhong Zhang & Yucheng Zhang, 2024. "A real spatial–temporal attention denoising network for nugget quality detection in resistance spot weld," Journal of Intelligent Manufacturing, Springer, vol. 35(6), pages 2743-2764, August.
    3. Jiajun Zhou & Liang Gao & Chao Lu & Xifan Yao, 2025. "Collaborative optimization of manufacturing service allocation via multi-task transfer learning evolutionary approach," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 1761-1779, March.
    4. 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.
    5. 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.
    6. Gurtej Singh Saini & AmirHossein Fallah & Pradeepkumar Ashok & Eric van Oort, 2022. "Digital Twins for Real-Time Scenario Analysis during Well Construction Operations," Energies, MDPI, vol. 15(18), pages 1-22, September.
    7. 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.
    8. Lan, Lan & Zhou, Zhifang, 2024. "Complementary or substitutive effects? The duality of digitalization and ESG on firm's innovation," Technology in Society, Elsevier, vol. 77(C).
    9. Yanzhi Zhao & Mingsi Zhao & Fengyu Shi, 2024. "Integrating Moral Education and Educational Information Technology: A Strategic Approach to Enhance Rural Teacher Training in Universities," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 15053-15093, September.
    10. 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.
    11. 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.
    12. Zhen Zhang & Zenan Yang & Chenchong Wang & Wei Xu, 2024. "Accelerating ultrashort pulse laser micromachining process comprehensive optimization using a machine learning cycle design strategy integrated with a physical model," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 449-465, January.
    13. Remigiusz Iwańkowicz & Radosław Rutkowski, 2023. "Digital Twin of Shipbuilding Process in Shipyard 4.0," Sustainability, MDPI, vol. 15(12), pages 1-27, June.
    14. Zhining Zhao & Hassan Alli & Masoud Ahmadipour & Rosalam Che me, 2024. "Sustainable agility of product development process based on a rough cloud technique: A case study on China’s small and medium enterprises," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-27, August.
    15. Fuwen Hu & Xianjin Qiu & Guoye Jing & Jian Tang & Yuanzhi Zhu, 2023. "Digital twin-based decision making paradigm of raise boring method," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2387-2405, June.
    16. 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).
    17. Leung, Eric K.H. & Lee, Carmen Kar Hang & Ouyang, Zhiyuan, 2022. "From traditional warehouses to Physical Internet hubs: A digital twin-based inbound synchronization framework for PI-order management," International Journal of Production Economics, Elsevier, vol. 244(C).
    18. SungKu Kang & Ran Jin & Xinwei Deng & Ron S. Kenett, 2023. "Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 415-428, February.
    19. Madalina CUC, 2021. "Improving The Decision-Making Process By Modeling Digital Twins In A Big Data Environment," Management and Marketing Journal, University of Craiova, Faculty of Economics and Business Administration, vol. 0(1), pages 138-154, May.
    20. Fromhold-Eisebith, Martina & Marschall, Philip & Peters, Robert & Thomes, Paul, 2021. "Torn between digitized future and context dependent past – How implementing ‘Industry 4.0’ production technologies could transform the German textile industry," Technological Forecasting and Social Change, Elsevier, vol. 166(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:36:y:2025:i:3:d:10.1007_s10845-024-02353-y. 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.