Extracting and identifying form features: a Bayesian approach

Introduces a new uncertainty reasoning-based method for the identification and extraction of manufacturing features from solid model descriptions of objects. A major difficulty faced by previously proposed methods for feature extraction has been the interaction between features. In interacting situations, the representation for various primitive features is non-unique, making their recognition very difficult. We develop an approach based on generating, propagating and combining geometrical and topological evidences in a hierarchical belief network for identifying and extracting features. The methodology combines and propagates evidences to determine a set of correct virtual links to be augmented in the cavity graph representing a depression of the object, so that the resulting supergraph can be partitioned to obtain the features of the object