  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.
