A neural network system feature recognition for two-dimensional

Recognition of geometric features is important for automatic evaluation of part designs and development of process plans. This paper describes an implementation of a neural network for feature recognition in sheet metal parts created in a CAD system. One major part of the implementation is the development of a rotation- and translationinsensitive encoding scheme which extracts critical information from geometric features and candidate geometric loops. The successful implementation has led to a powerful system where end users can customize the domain offeatures that can be recognized. Training of the neural network memory is also achieved through a user-friendly graphic interface.