Surface shape-based clustering for B-rep models

Jihua Wang

doi:10.1007/s11042-020-09252-3

2020-07-06

PRE

Jihua Wang

Wei Yan *

Chao Huang

DOI：10.1007/s11042-020-09252-3

原文链接

原文求助

摘要

En 中文

Matching, clustering, and retrieving 3D CAD models of mechanical components based on their shape are useful for many CAD/CAM applications such as design reuse, variant process planning and group technology. Surfaces are the prominent elements of the B-Rep (Boundary Representation) CAD model, but the current methods of similarity assessment are not centered on the surfaces and lack an accurate description of their geometric features. In order to solve the problem of retrieval and clustering of B-Rep models more efficiently, the concept of most crucial surface is proposed and its corresponding characteristics are studied in detail. The contribution of our approach is that surfaces are the major shape determinants of the B-Rep model, and the distribution of Carosati curvatures is the optimum shape features of surfaces. First, the surface elements are extracted from the STEP (Standard for Exchange of Product Model) files of the B-rep models, and the distribution of minimum, Gauss and Carosati surface curvatures are converted into the shape feature space by the wavelet transform, the Fourier transform, and the grouping calculation. Thus we characterize the B-Rep model as a histogram with surfaces as bins, and then compare and cluster the B-Rep models by the bipartite matching algorithm or the earth mover's distance. The surface-based methods are evaluated with the four effectiveness indices in the clustering experiment of the NDR (National Design Reservoir) data, and the results indicated that the grouping method for the surface Carosati curvatures has a highly competent matching and clustering performance.

Keyword:

Surface shape

B-rep model

Surface curvature

3D model clustering

STEP(standard for exchange of product model)

Bipartite graph matching

Earth mover's distance