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Reconstruction of 3D surfaces from incomplete digitisations using statistical shape models for manufacturing processes

Author

Listed:
  • José M. Navarro-Jiménez

    (Institut de Calcul Intensif at Ecole Centrale de Nantes)

  • José V. Aguado

    (Institut de Calcul Intensif at Ecole Centrale de Nantes)

  • Grégoire Bazin

    (Stelia Aerospace)

  • Vicente Albero

    (Universitat Jaume I)

  • Domenico Borzacchiello

    (Institut de Calcul Intensif at Ecole Centrale de Nantes)

Abstract

Digitization of large parts with tight geometric tolerances is a time-consuming process that requires a detailed scan of the outer surface and the acquisition and processing of massive data. In this work, we propose a methodology for fast digitization using a partial scan in which large regions remain unmeasured. Our approach capitalizes on a database of fully scanned parts from which we extract a low-dimensional description of the shape variability using Statistical Shape Analysis. This low-dimensional description allows an accurate representation of any sample in the database with few independent parameters. Therefore, we propose a reconstruction algorithm that takes as input an incomplete measurement (faster than a complete digitization), identifies the statistical shape parameters and outputs a full scan reconstruction. We showcase an application to the digitization of large aeronautical fuselage panels. A statistical shape model is constructed from a database of 793 shapes that were completely digitized, with a point cloud of about 16 million points for each shape. Tests carried out at the manufacturing facility showed an overall reduction in the digitization time by 80% (using a partial digitization of 3 million points per shape) while keeping a high accuracy (reconstruction precision of 0.1 mm) on the reconstructed surface.

Suggested Citation

  • José M. Navarro-Jiménez & José V. Aguado & Grégoire Bazin & Vicente Albero & Domenico Borzacchiello, 2023. "Reconstruction of 3D surfaces from incomplete digitisations using statistical shape models for manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2345-2358, June.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:5:d:10.1007_s10845-022-01918-z
    DOI: 10.1007/s10845-022-01918-z
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    References listed on IDEAS

    as
    1. Ruiyang Hao & Bingyu Lu & Ying Cheng & Xiu Li & Biqing Huang, 2021. "A steel surface defect inspection approach towards smart industrial monitoring," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1833-1843, October.
    2. Syed Hammad Mian & Abdulrahman Al-Ahmari, 2019. "Comparative analysis of different digitization systems and selection of best alternative," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2039-2067, June.
    3. Domen Tabernik & Samo Šela & Jure Skvarč & Danijel Skočaj, 2020. "Segmentation-based deep-learning approach for surface-defect detection," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 759-776, March.
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