Reverse engineering of CAD models via clustering and approximate implicitization

In applications like computer aided design, geometric models are often represented numerically as polynomial splines or NURBS, even when they originate from primitive geometry. For purposes such as redesign and isogeometric analysis, it is of interest to extract information about the underlying geom...

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Vydané v:Computer aided geometric design Ročník 80; s. 101876
Hlavní autori: Raffo, Andrea, Barrowclough, Oliver J.D., Muntingh, Georg
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.06.2020
Predmet:
Approximate implicitization
Clustering
Reverse engineering
Statistical learning
Approximate implicitization
Clustering
Reverse engineering
Statistical learning
ISSN:0167-8396, 1879-2332
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Popis
Shrnutí:In applications like computer aided design, geometric models are often represented numerically as polynomial splines or NURBS, even when they originate from primitive geometry. For purposes such as redesign and isogeometric analysis, it is of interest to extract information about the underlying geometry through reverse engineering. In this work we develop a novel method to determine these primitive shapes by combining clustering analysis with approximate implicitization. The proposed method is automatic and can recover algebraic hypersurfaces of any degree in any dimension. In exact arithmetic, the algorithm returns exact results. All the required parameters, such as the implicit degree of the patches and the number of clusters of the model, are inferred using numerical approaches in order to obtain an algorithm that requires as little manual input as possible. The effectiveness, efficiency and robustness of the method are shown both in a theoretical analysis and in numerical examples implemented in Python. •The introduction of a novel algorithm to group patches of CAD models with respect to the underlying primitive geometry.•A theoretical study of the stability and the computational complexity of the method.•The validation of the method on both synthetic and real data.
ISSN:0167-8396
1879-2332
DOI:10.1016/j.cagd.2020.101876