A neural network method for recognition of part orientation in a bowl feeder

보울 피이더에서 신경 회로망을 이용한 부품 자세 인식에 관한 연구

A neural network method is applied for recognizing the orientation o f individual parts being fed from a bowl feeder. The system is designed in such a way that a part can be discriminated and sorting according to every possible stable orientation without implementing any a mechanical tooling. The operation of the bowl feeder is based on a 2D image obtained from an array of fiber optic sensor located on the feeder track. The acquired binary image of a moving and vibrating part is used as input to a neural network which, in turn, determines t he orientation of the part. The main task of the neural network, here is to synthesize the appropriate internal discriminant functions for the part orientation using the part features. A series of the experiments reveals several promising points on performance. Since the operation of the feeder is highly programmable, it is well suited for feeding and sorting small parts prior to small batch assembly work.