A Set-based Approach for Feature Extraction of 3D CAD Models
Peng Xu, Qi Gao, Ying-Jie Wu
TL;DR
The paper tackles automatic feature extraction from 3D CAD models under inherent geometric uncertainty. It proposes a set-based approach that converts ambiguity into a collection of feature subgraphs by defining convexity for faces, edges, and vertices and modeling the model with a two-level TAAG to capture boundary relationships. A two-stage workflow identifies feature boundaries and builds feature subgraphs, enabling robust preprocessing for downstream feature recognition using a pattern library. Implemented in C++ with UG/Open NX and validated on B-REP models, the method demonstrates feasible extraction of both convex and concave subgraphs, offering a flexible, uncertainty-aware path toward feature recognition in manufacturing contexts.
Abstract
Feature extraction is a critical technology to realize the automatic transmission of feature information throughout product life cycles. As CAD models primarily capture the 3D geometry of products, feature extraction heavily relies on geometric information. However, existing feature extraction methods often yield inaccurate outcomes due to the diverse interpretations of geometric information. This report presents a set-based feature extraction approach to address this uncertainty issue. Unlike existing methods that seek accurate feature results, our approach aims to transform the uncertainty of geometric information into a set of feature subgraphs. First, we define the convexity of basic geometric entities and introduce the concept of two-level attributed adjacency graphs. Second, a feature extraction workflow is designed to determine feature boundaries and identify feature subgraphs from CAD models. This set of feature subgraphs can be used for further feature recognition. A feature extraction system is programmed using C++ and UG/Open to demonstrate the feasibility of our proposed approach.