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.
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Acknowledgements
This work is supported by Natural Science Foundation of Zhejiang Province (Grant No. LQ22E050017), Zhejiang Science and Technology Plan Project (Grant No. 2018C01003), National Natural Science Foundation of China (Grant No. 52275499), and National Key Research and Development Program of China (Grant No. 2022YFF0605700).
Funding
This work is funded by Natural Science Foundation of Zhejiang Province (Grant No. LQ22E050017), Zhejiang Science and Technology Plan Project (Grant No. 2018C01003), National Natural Science Foundation of China (Grant No. 52275499), and National Key Research and Development Program of China (Grant No. 2022YFF0605700).
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Yiping Shao contributed to propose ideas and methods, conduct experimental verification, and write the manuscript. Jun Chen contributed to data collection, method validation, and write the manuscript. Xiaoli Gu contributed to method investigation and visualization. Jiansha Lu contributed to propose ideas, supervise method, manuscript reviewing and checking. Shichang Du supervises methods, funding acquisition, and project administration. All authors have read and agreed to the published version of the manuscript.
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Shao, Y., Chen, J., Gu, X. et al. A novel curved surface profile monitoring approach based on geometrical-spatial joint feature. J Intell Manuf (2024). https://doi.org/10.1007/s10845-024-02349-8
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DOI: https://doi.org/10.1007/s10845-024-02349-8