Abstract
A self-organizing neural network, ART2, based on adaptive resonance theory (ART), is applied to the problem of feature recognition from a boundary representation (B-rep) solid model. A modified face score vector calculation scheme is adopted to represent the features by continuous-valued vectors, suitable to be input to the network. The face score is a measure of the face complexity based upon the convexity or concavity of the surrounding region. The face score vector depicts the topological relations between a face and its neighbouring faces. The ART2 network clusters similar features together. The similarity of the features within a cluster is controlled by a vigilance parameter. A new feature presented to the net is associated with one of the existing clusters, if the feature is similar to the members of the cluster. Otherwise, the net creates a new cluster. An algorithm of the ART2 network is implemented and tested with nine different features. The results obtained indicate that the network has significant potential for application to the problem of feature recognition.
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LANKALAPALLI , K., CHATTERJEE , S. & CHANG , T. Feature recognition using ART2: a self-organizing neural network. Journal of Intelligent Manufacturing 8, 203–214 (1997). https://doi.org/10.1023/A:1018521207901
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DOI: https://doi.org/10.1023/A:1018521207901