Grouping parts with a neural network

Abstract Recognition of objects is used for identification, classification, verification, and inspection tasks in manufacturing. Neural networks are well suited for this application. In this paper, an application of a back-propagation neural network for the grouping of parts is presented. The back-propagation neural network is provided with binary images describing geometric part shapes, and it generates part families. To decrease the chance of reaching a local optimum and to speed up the computation process, three parameters—bias, momentum, and learning rate—are taken into consideration. The contribution of this paper is in design of a neuro-based system to group parts. The network groups all the training and testing parts into part families with perfect accuracy. Performance of the system has been tested on a benchmark example and then by experimenting with 60 parts.

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