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New methodology for the characterization of 3D model reconstructions to meet conditions of input data and requirements of downstream application
Published online by Cambridge University Press: 16 May 2024
Abstract
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In the field of 3D model reconstruction, manifold methods have been developed that derive CAD models from 3D scan data. Opposed to classical CAD modelling, where surface and solid modelling exist, a further diversification of modelling techniques is observed, caused by different methods to build up the geometry. This research introduces a new classification, the so-called Level of Complexities. It can be applied to the complete Reverse Engineering process chain and lays the foundation for further research on how to match requirements arising from all process steps and downstream applications.
Keywords
- Type
- Design Methods and Tools
- Information
- Creative Commons
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.- Copyright
- The Author(s), 2024.
References
Aranburu, A.; Justel, D.; Angulo, I. (2020): Reusability and Flexibility in Parametric Surface-based Models: A Review of Modelling Strategies. In: Computer-Aided Design and Applications, 18 (4), pp. 864–874. https://doi.org/10.14733/cadaps.2021.864-874.CrossRefGoogle Scholar
Ben Makhlouf, A.; Louhichi, B.; Mahjoub, M. A.; Deneux, D. (2019): Reconstruction of a CAD model from the deformed mesh using B-spline surfaces. In: International Journal of Computer Integrated Manufacturing, 32 (7), pp. 669–681. https://doi.org/10.1080/0951192X.2019.1599442.CrossRefGoogle Scholar
Bénière, R.; Subsol, G.; Gesquière, G.; Le Breton, F.; Puech, W. (2013): A comprehensive process of reverse engineering from 3D meshes to CAD models. In: Computer-Aided Design, 45 (11), pp. 1382–1393. https://doi.org/10.1016/j.cad.2013.06.004.Google Scholar
Brujic, D.; Ainsworth, I.; Ristic, M. (2011): Fast and accurate NURBS fitting for reverse engineering. In: The International Journal of Advanced Manufacturing Technology, 54 (5-8), pp. 691–700. https://doi.org/10.1007/s00170-010-2947-1.CrossRefGoogle Scholar
Buonamici, F.; Carfagni, M.; Furferi, R.; Governi, L.; Lapini, A.; Volpe, Y. (2018): Reverse engineering modeling methods and tools: a survey. In: Computer-Aided Design and Applications, 15 (3), pp. 443–464. https://doi.org/10.1080/16864360.2017.1397894.CrossRefGoogle Scholar
Chinn, A. (2021): New approach for accurate, robust morphing of CAD geometries. Facilitates bi-di transfer between analysis-testmanufacture and design. Available at https://www.enginsoft.com/expertise/new-approach-for-accurate,-robust-morphing-of-cad-geometries.html, accessed 13.11.2021.Google Scholar
Ebrahim, M. A.-B. (2015): 3D Laser Scanners’ Techniques Overview. In: International Journal of Science and Research (IJSR)).Google Scholar
Eck, M.; Hoppe, H. (1996): Automatic reconstruction of B-spline surfaces of arbitrary topological type. In: SIGGRAPH '96: Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pp. 325–334. https://doi.org/10.1145/237170.237271.CrossRefGoogle Scholar
Farin, G.; Hansford, D. (1999): Discrete Coons patches. In: Computer Aided Geometric Design, 16 (7), pp. 691–700. https://doi.org/10.1016/S0167-8396(99)00031-X.CrossRefGoogle Scholar
Krishnamurthy, V.; Levoy, M. (1996): Fitting Smooth Surfaces to Dense Polygon Meshes. In: SIGGRAPH '96: Proceedings of the 23rd annual conference on Computer graphics and interactive techniques. https://doi.org/10.1145/237170.237270.CrossRefGoogle Scholar
Lee, H.; Lee, J.; Kim, H.; Mun, D. (2021): Dataset and method for deep learning-based reconstruction of 3D CAD models containing machining features for mechanical parts. In: Journal of Computational Design and Engineering, 9 (1), pp. 114–127. https://doi.org/10.1093/jcde/qwab072.CrossRefGoogle Scholar
Sederberg, M. T.; Sederberg, T. W. (2010): T-Splines: A Technology for Marine Design with Minimal Control Points. In: Chesapeake Powerboat Symposium.CrossRefGoogle Scholar
Shi, P.; Qi, Q.; Qin, Y.; Scott, P. J.; Jiang, X. (2020): A novel learning-based feature recognition method using multiple sectional view representation. In: Journal of Intelligent Manufacturing, 31 (5), pp. 1291–1309. https://doi.org/10.1007/s10845-020-01533-w.CrossRefGoogle Scholar
Soni, K.; Chen, D.; Lerch, T. (2009): Parameterization of prismatic shapes and reconstruction of free-form shapes in reverse engineering. In: The International Journal of Advanced Manufacturing Technology, 41 (9-10), pp. 948–959. https://doi.org/10.1007/s00170-008-1550-1.CrossRefGoogle Scholar
Varady, T. (2008): Automatic Procedures to Create CAD Models from Measured Data. In: Computer-Aided Design and Applications, 5 (5), pp. 577–588. https://doi.org/10.3722/cadaps.2008.577-588.CrossRefGoogle Scholar
Wang, J.; Gu, D.; Yu, Z.; Tan, C.; Zhou, L. (2012): A framework for 3D model reconstruction in reverse engineering. In: Computers & Industrial Engineering, 63 (4), pp. 1189–1200. https://doi.org/10.1016/j.cie.2012.07.009.CrossRefGoogle Scholar
Wei, J.; Lin, G.; Yap, K.-H.; Hung, T.-Y.; Xie, L. (2020): Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds.CrossRefGoogle Scholar
Zhang, Z.; Jaiswal, P.; Rai, R. (2018): FeatureNet: Machining feature recognition based on 3D Convolution Neural Network. In: Computer-Aided Design, 101 (6), pp. 12–22. https://doi.org/10.1016/j.cad.2018.03.006.CrossRefGoogle Scholar
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