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Reconstruction of 3D surfaces from incomplete digitisations using statistical shape models for manufacturing processes
dc.contributor.author | Navarro Jiménez, José Manuel | |
dc.contributor.author | Aguado, José V. | |
dc.contributor.author | Bazin, Grégoire | |
dc.contributor.author | Albero, Vicente | |
dc.contributor.author | Borzacchiello, Domenico | |
dc.date.accessioned | 2022-09-23T11:04:54Z | |
dc.date.available | 2022-09-23T11:04:54Z | |
dc.date.issued | 2022-03-18 | |
dc.identifier.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-z | ca_CA |
dc.identifier.issn | 0956-5515 | |
dc.identifier.issn | 1572-8145 | |
dc.identifier.uri | http://hdl.handle.net/10234/199769 | |
dc.description.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. | ca_CA |
dc.format.extent | 24 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Springer | ca_CA |
dc.relation.isPartOf | Journal of Intelligent Manufacturing, 34 (2023) | |
dc.rights | © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | statistical shape analysis | ca_CA |
dc.subject | shape reconstruction | ca_CA |
dc.subject | surface digitization | ca_CA |
dc.subject | sparse sampling | ca_CA |
dc.title | Reconstruction of 3D surfaces from incomplete digitisations using statistical shape models for manufacturing processes | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1007/s10845-022-01918-z | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/acceptedVersion | ca_CA |
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