IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v36y2025i3d10.1007_s10845-024-02349-8.html
   My bibliography  Save this article

A novel curved surface profile monitoring approach based on geometrical-spatial joint feature

Author

Listed:
  • Yiping Shao

    (Zhejiang University of Technology)

  • Jun Chen

    (Zhejiang University of Technology)

  • Xiaoli Gu

    (Zhejiang Lab)

  • Jiansha Lu

    (Zhejiang University of Technology)

  • Shichang Du

    (Shanghai Jiao Tong University)

Abstract

With the development of high-end manufacturing, a variety of sophisticated parts with complex curved surfaces have emerged, and curved surface profile monitoring is of great importance for achieving the higher performance of a part. Benefiting from the recent advancements in non-contact measurement systems, millions of high-density point clouds are rapidly collected to represent the entire curved surface, which can reflect the geometrical and spatial features. The traditional discrete key quality characteristics-based monitoring approaches are not capable of handling complex curved surfaces. A novel curved surface profile monitoring approach based on geometrical-spatial joint features is proposed, which consists of point cloud data preprocessing, Laplace–Beltrami spectrum calculation, spatial geodesic clustering degree definition, and multivariate control chart construction. It takes full advantage of the entire wealth information on complex curved surfaces and can detect the small shifts of geometrical shape and spatial distribution information of non-Euclidean surfaces. Two real-world engineering surfaces case studies illustrate the proposed approach is effective and feasible.

Suggested Citation

  • Yiping Shao & Jun Chen & Xiaoli Gu & Jiansha Lu & Shichang Du, 2025. "A novel curved surface profile monitoring approach based on geometrical-spatial joint feature," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 2055-2077, March.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:3:d:10.1007_s10845-024-02349-8
    DOI: 10.1007/s10845-024-02349-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-024-02349-8
    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/s10845-024-02349-8?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. Linmiao Zhang & Kaibo Wang & Nan Chen, 2016. "Monitoring wafers’ geometric quality using an additive Gaussian process model," IISE Transactions, Taylor & Francis Journals, vol. 48(1), pages 1-15, January.
    2. Faping Zhang & Jialun Zhang & Junjiu Ma, 2023. "Data-manifold-based monitoring and anomaly diagnosis for manufacturing process," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3159-3177, October.
    3. Lee J. Wells & Romina Dastoorian & Jaime A. Camelio, 2021. "A novel NURBS surface approach to statistically monitor manufacturing processes with point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 329-345, February.
    4. Chen Zhao & Shichang Du & Jun Lv & Yafei Deng & Guilong Li, 2023. "A novel parallel classification network for classifying three-dimensional surface with point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 515-527, February.
    5. Romina Dastoorian & Lee J. Wells, 2023. "A hybrid off-line/on-line quality control approach for real-time monitoring of high-density datasets," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 669-682, February.
    6. Jaeseung Baek & Myong K. Jeong & Elsayed A. Elsayed, 2023. "Monitoring variations in multimode surface topography," International Journal of Production Research, Taylor & Francis Journals, vol. 61(4), pages 1129-1145, February.
    7. Saumuy Suriano & Hui Wang & Chenhui Shao & S. Jack Hu & Praveen Sekhar, 2015. "Progressive measurement and monitoring for multi-resolution data in surface manufacturing considering spatial and cross correlations," IISE Transactions, Taylor & Francis Journals, vol. 47(10), pages 1033-1052, October.
    8. Zhen He & Ling Zuo & Min Zhang & Fadel M. Megahed, 2016. "An image-based multivariate generalized likelihood ratio control chart for detecting and diagnosing multiple faults in manufactured products," International Journal of Production Research, Taylor & Francis Journals, vol. 54(6), pages 1771-1784, March.
    9. Anh Tuan Bui & Daniel W. Apley, 2022. "Robust monitoring of stochastic textured surfaces," International Journal of Production Research, Taylor & Francis Journals, vol. 60(16), pages 5071-5086, August.
    10. Kaibo Wang & Wei Jiang & Bo Li, 2016. "A spatial variable selection method for monitoring product surface," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4161-4181, July.
    11. Tingting Huang & Shanggang Wang & Shunkun Yang & Wei Dai, 2021. "Statistical process monitoring in a specified period for the image data of fused deposition modeling parts with consistent layers," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2181-2196, December.
    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. Jaeseung Baek & Myong K. Jeong & Elsayed A. Elsayed, 2024. "Spatial randomness-based anomaly detection approach for monitoring local variations in multimode surface topography," Annals of Operations Research, Springer, vol. 341(1), pages 173-195, October.
    2. Romina Dastoorian & Lee J. Wells, 2023. "A hybrid off-line/on-line quality control approach for real-time monitoring of high-density datasets," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 669-682, February.
    3. Chen Zhao & Shichang Du & Jun Lv & Yafei Deng & Guilong Li, 2023. "A novel parallel classification network for classifying three-dimensional surface with point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 515-527, February.
    4. Xin Wang & Xinchao Jiang & Hu Wang & Guangyao Li, 2025. "Manifold learning-assisted uncertainty quantification of system parameters in the fiber metal laminates hot forming process," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 2193-2219, March.
    5. Jie Guo & Hao Yan & Chen Zhang, 2024. "Thompson Sampling-Based Partially Observable Online Change Detection for Exponential Families," INFORMS Joural on Data Science, INFORMS, vol. 3(2), pages 145-161, October.
    6. Matteo Bugatti & Bianca Maria Colosimo, 2022. "Towards real-time in-situ monitoring of hot-spot defects in L-PBF: a new classification-based method for fast video-imaging data analysis," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 293-309, January.
    7. Douglas A. J. Brion & Sebastian W. Pattinson, 2022. "Generalisable 3D printing error detection and correction via multi-head neural networks," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    8. Yuquan Meng & Yuhang Yang & Haseung Chung & Pil-Ho Lee & Chenhui Shao, 2018. "Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review," Sustainability, MDPI, vol. 10(12), pages 1-28, December.
    9. Sangahn Kim & Mehmet Turkoz & Myong K. Jeong & Elsayed A. Elsayed, 2024. "Monitoring of group-structured high-dimensional processes via sparse group LASSO," Annals of Operations Research, Springer, vol. 340(2), pages 891-911, September.
    10. Kaishu Xia & Thorsten Wuest & Ramy Harik, 2023. "Automated manufacturability analysis in smart manufacturing systems: a signature mapping method for product-centered digital twins," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3069-3090, October.

    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_s10845-024-02349-8. 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.