A Dempster-Shafer approach for recognizing machine features from CAD models

This paper introduces an evidential reasoning-based approach for recognizing and extracting manufacturing features from solid model description of objects. A major di4culty faced by previously proposed methods for feature extraction has been the interaction between features due to non-uniqueness and ambiguousness in feature representation. To overcome this di4culty, we introduce a Dempster–Shafer approach for generating and combining geometric and topologic evidences to identify and extract interacting features. Themain contributions of this research include introducing di6erent classes of evidences based on the geometric and topologic relationships at di6erent abstraction levels for e6ective evidential reasoning and developing the principle of association to overcome the mutual exclusiveness assumption of the Dempster–Shafer theory. Experiments demonstrate the e6ectiveness of the proposed approach in extracting interacting machine features. ? 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.