Abstract
B-Rep (Boundary Representation) CAD model is widely used in the representation of manufactured product in computer, and it is a kind of real 3D structure with invisible part relative to 2.5D mesh model, so the shape feature recognition of B-Rep model is worth of much studying. We present one approach of shape feature recognition of B-Rep model based on the wavelet transform of surface boundary and region; it is inspired by the neuropsychology view that surface is the key visual features and by the systematology method that an object is recognized by decomposing and grouping its similar parts. Surface elements of B-Rep model are extracted from the neutral STEP (Standard for Exchange of Product Model Data) file; the curvatures of surface boundary and region were decomposed by wavelet transform, and then the coefficient statistics of same scale were as the surface feature vector. Similar surfaces of B-Rep model were clustered as a bin with the sum of perimeters and the mean vector, and all bins constituting a histogram are finally as the feature vector of B-Rep model. Thus B-Rep models are compared and retrieved using the EMD (Earth Mover’s Distance) of histogram. Our approach was evaluated by retrieval experiment with NDR (National Design Reservoir), and the result indicated its highly competent performance.
Highlights
We present one approach of shape feature recognition of B-Rep model based on the wavelet transform of surface boundary and region; it is inspired by the neuropsychology view that surface is the key visual features and by the systematology method that an object is recognized by decomposing and grouping its similar parts
Shape feature recognition of CAD model lays the solid foundation for content-based model retrieval, functions and structures mapping, design intent analysis throughout industrial product design, and manufacture field
Comparing with the mesh model as 2.5D model that only gives us overall visible appearance, B-Rep model as real 3D model has unambiguous internal schemes and contains rich design semantics such as geometric and topology variables, and it can be precisely expressed in the form of attributed adjacency graph (AAG) [1, 2]
Summary
Shape feature recognition of CAD model lays the solid foundation for content-based model retrieval, functions and structures mapping, design intent analysis throughout industrial product design, and manufacture field. B-Rep model is almost only representation way of CAD model related to manufactured objects, which is widely shared and exchanged in STEP format. It is meaningful to extract the shape features of B-Rep model for supporting recognition, classification, and retrieval, even transforming B-Rep model into high-level knowledge representation from low-level geometry descriptor [3]. B-Rep model is designed and stored in proprietary format in different CAD platform and transformed into a neutral STEP file for transferring, sharing, exchanging, and displaying in heterogeneous CAD environments. BRep model in STEP format is a hierarchy with geometric and topological elements of solid, shell, surface, loop, and edge
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