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Thin part identification for CAD model classification

Jean-Philippe Pernot (Arts et Metiers ParisTech, CNRS-LSIS, Aix-en-Provence, France)
Franca Giannini (IMATI-CNR, Consiglio Nazionale delle Ricerche, Genoa, Italy)
Cédric Petton (Laboratory LSIS - UMR CNRS 7296, Arts et Métiers ParisTech, Aix-en-Provence, FRANCE)

Engineering Computations

ISSN: 0264-4401

Article publication date: 2 March 2015




The purpose of this paper is to focus on the characterization and classification of parts with respect to the meshing issue, and notably the meshing of thin parts difficulty handled automatically and which often requires adaptation steps. The objective is to distinguish the so-called thin parts and parts with thin features from the other parts.


The concepts of thin part and part with thin features are introduced together with the mechanisms and criteria used for their identification in a CAD models database. The criteria are built on top of a set of shape descriptors and notably the distance distribution which is used to characterize the thickness of the object. To speed up the identification process, shape descriptors are computed from tessellated parts.


A complete modular approach has been designed. It computes shape descriptors over parts stored in a directory and it uses criteria to distinguish three categories: thin parts, parts with thin features and other parts. Being the three categories identified, the user can spend more time on the parts that are considered as more difficulty meshable.

Research limitations/implications

The approach is limited to the three above mentioned categories. However, it has been designed so that the values corresponding to the shape descriptors and associated meshing qualities can easily be inserted within a machining learning tool later on.

Practical implications

The use of the developed tool can be seen as a pre-processing step during the preparation of finite element (FE) simulation models. It is automatic and can be run in batch and in parallel.


The approach is modular, it is simple and easy to implement. Categories are built on top of several shape descriptors and not on a unique signature. It is independent of the CAD modeler. This approach is integrated within a FE simulation model preparation framework and help engineers anticipating difficulties when meshing CAD models.



The work has been partially supported by the VISIONAIR project funded by the European Commission under grant agreement 262044, and by the project MIUR Fabbrica del Future SuFSeF Sustainable Factory SEmantic Framework.


Pernot, J.-P., Giannini, F. and Petton, C. (2015), "Thin part identification for CAD model classification", Engineering Computations, Vol. 32 No. 1, pp. 62-85.



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Copyright © 2015, Emerald Group Publishing Limited

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