Sequence-dependent clustering of parts and machines: A Fuzzy ART neural network approach

Abstract

This study addresses the problem of identifying families of parts having a similar sequence of operations. This is a prerequisite for the implementation of cellular manufacturing, group technology, just-in-time manufacturing systems, and for streamlining material flows in general. A pattern recognition approach based on artificial neural networks is proposed, and it is shown that the Fuzzy ART neural network can be effectively utilized for this application. First, a representation scheme for operation sequences is developed, followed by an illustrative example. A more comprehensive experimental verification, based on the mixture-model approach is then performed to evaluate its performance. The experimental factors include size of the part-machine matrix, proportion of voids, proportion of exceptional elements, and vigilance threshold. It is shown that this neural network is effective in identifying good clustering solutions, consistently and with relatively fast execution times.