Feature Recognition Based on Neural Network

An efficient methodology for recognizing features is presented. The part information is taken from the B-rep solid date library then broken down into sub-graph. Once the sub-graphs are generated, they are first checked to see whether they match with the predefined feature library. If so, a feature vector is assigned to them. Otherwise, base faces are obtained as heuristic information and used to restore missing faces, meanwhile, update the sub-graphs. The sub-graphs are transformed into vectors, and these vectors are presented to the neural network which classifies them into feature classes.