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Grouping parts based on geometrical shapes and manufacturing attributes using a neural network

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Abstract

This paper proposes a new part family classification system (IPFACS: Image Processing and Fuzzy ART based Clustering System), which incorporates image processing techniques and a modified Fuzzy ART neural network algorithm. IPFACS can classify parts based on geometrical shape and manufacturing attributes, simultaneously. With a proper reduction and normalization of an image data through the image processing methods and scaling capability in the modified Fuzzy ART, different types of geometrical shape data and manufacturing attribute data can be simultaneously classified in the same system. IPFACS has been tested for an example set of hypothetical parts. The results show that IPFACS provides a good feasible approach to form part families based on both geometrical shape and manufacturing attributes.

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Authors and Affiliations

  1. Department of Industrial Engineering, The Kwandong University, San 7 Imchun Ri, Yangyang Eub, Yangyang Kun, Kangwondo, 215–800, Korea

    SUNG YOUL LEE

  2. Department of Industrial Engineering, College of Engineering, The University of Iowa, Iowa City, IA, 52242, USA

    G. W. FISCHER

Authors
  1. SUNG YOUL LEE
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  2. G. W. FISCHER
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LEE, S.Y., FISCHER, G.W. Grouping parts based on geometrical shapes and manufacturing attributes using a neural network. Journal of Intelligent Manufacturing 10, 199–209 (1999). https://doi.org/10.1023/A:1008932922695

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  • DOI: https://doi.org/10.1023/A:1008932922695

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