Abstract
Algorithms using analytic methods for the group technology problem of machine part family formation are relatively slow considering the typical large size of the machine-part matrix. In this study, a neural network approach that uses the Adaptive Resonance Theory (ART) paradigm to classify vectors obtained from the machine-part matrix, is proposed. The effect of varying the sensitivity of the ART in recognizing the similarity between the vectors and preprocessing of these vectors, on the formation of part groups is discussed. Initial test results obtained from the neural network application are compared with those of existing cell formation algorithms.
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© 1991 Springer-Verlag Berlin Heidelberg
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Huggahalli, R., Dagli, C. (1991). Machine-Part Family Formation Using Neural Networks. In: Dwivedi, S.N., Verma, A.K., Sneckenberger, J.E. (eds) CAD/CAM Robotics and Factories of the Future ’90. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-84338-9_13
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DOI: https://doi.org/10.1007/978-3-642-84338-9_13
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