Reconstruction of 3D surfaces from incomplete digitisations using statistical shape models for manufacturing processes
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7035
comunitat-uji-handle3:10234/8617
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INVESTIGACIONMetadata
Title
Reconstruction of 3D surfaces from incomplete digitisations using statistical shape models for manufacturing processesAuthor (s)
Date
2022-03-18Publisher
SpringerISSN
0956-5515; 1572-8145Bibliographic citation
Navarro-Jiménez, J.M., Aguado, J.V., Bazin, G. et al. Reconstruction of 3D surfaces from incomplete digitisations using statistical shape models for manufacturing processes. Journal of Intelligent Manufacturing 34, 2345–2358 (2023). https://doi.org/10.1007/s10845-022-01918-zType
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/acceptedVersionAbstract
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 ... [+]
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. [-]
Is part of
Journal of Intelligent Manufacturing, 34 (2023)Rights
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
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info:eu-repo/semantics/openAccess
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- EMC_Articles [822]