ResearchNeural network approach to the reconstruction of freeform surfaces for reverse engineering
References (14)
Free-form shapes: an integrated CAD/CAM system
Comput. Indust.
(1984)- et al.
Curve and surface constructions using rational B-splines
Comput.-Aided Des.
(1987) - et al.
CASCAM — an automated system for sculptured surface cavity machining
Comput. Indust.
(1991) - et al.
Non-contact 3-D digitizing and machining system for free-form surfaces
Ann. CIRP
(1991) - et al.
Matrix representation for NURBS curves and surfaces
Comput.-Aided Des.
(1990) - et al.
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2022, Journal of Computational ScienceCitation Excerpt :Another approach is based on the use of partial differential equations (PDEs) for surface reconstruction; see [9] for an overview on the field. In recent years, different machine learning methods, mostly based on neural networks and deep learning [10,11], metaheuristic techniques such as genetic algorithms and nature-inspired optimization algorithms [12,13], or combinations of both [14] have also been used for 3D reconstruction from point clouds. In this section, we will briefly review some of the recent works, which are closely related to this paper.
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2018, Computer Methods in Applied Mechanics and EngineeringCitation Excerpt :The same analogy could be used in shape fitting but it entails excessive algorithm complexity which makes it less suitable in shape optimization. Neural networks are employed as the base framework in reconstruction of free-form surfaces for reverse engineering by Gu, P. and X. Yan [30]. Barhak, J. and Fischer, A [31] also used neural network and PDE techniques in parameterization and reconstruction from 3D scattered points.
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2016, Applied Mathematics and ComputationCitation Excerpt :Unfortunately, the optimization problems given by all these approaches are very difficult and cannot be generally solved by traditional mathematical optimization techniques [22]. Interesting research carried out during the last two decades has shown that the application of Artificial Intelligence techniques can achieve remarkable results for optimization problems [4,7,8,40–43]. Most of these methods rely on some kind of neural networks, such as multi-layered neural networks with modified back-propagation algorithms [40], or Kohonen’s SOM (Self-Organizing Maps) nets [41].
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