A Set-based Approach for Feature Extraction of 3D CAD Models

TL;DR

This paper introduces a set-based method for extracting features from 3D CAD models, transforming geometric uncertainties into feature subgraphs to improve robustness in feature recognition.

Contribution

The paper proposes a novel set-based approach that converts geometric uncertainty into feature subgraphs, enhancing feature extraction accuracy from CAD models.

Findings

Successfully implemented in C++ and UG/Open

Effectively handles geometric uncertainty

Produces reliable feature subgraphs

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.