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Proceedings of CAD'14, 2014, 88-90
Applying Database Optimization Technologies to Feature Recognition in CAD

Zhibin Niu, Ralph Martin, Frank C Langbein, Cardiff University
Malcolm Sabin, Numerical Geometry Ltd.
Henry Bucklow, TranscenData Europe Ltd.

Abstract. Feature recognition is important in analysis or simulation based on a CAD model; this is done by first meshing the model. Real industrial models normally have many small details, and in many cases, their effect on analysis is minor. Suppressing such details allows meshing to be both quicker and more robust, and as a sparser mesh with larger elements results, the time needed for analysis is reduced. Feature recognition can help to find candidates for removal. In computer-aided manufacturing, computer-aided process planning uses use feature information to generate a sequence of instructions to manufacture a model.  Finding features by hand is tedious: automatically finding features as candidates for removal is preferable. Much research has been devoted to this topic. Our approach to feature recognition is based on a high-level declarative feature definition language, which allows users to define new kinds of features to be found. The users merely need to specify what constitutes a feature, rather than a specific algorithm for finding instances of it. Different applications need different definitions of features: parts of a shape which are important for machining may be quite different to those which can be ignored for analysis for example: features important for heat analysis may be quite different from those for electrostatic analysis.

Keywords. Computer-aided design, feature recognition, database optimization

DOI: 10.14733/cadconfP.2014.88-90