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    Using Neural Networks to Generate Design Similarity Measures

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    TR_99-38.pdf (91.39Kb)
    No. of downloads: 477

    Date
    1999
    Author
    Balasubramanian, Sundar
    Herrmann, Jeffrey W.
    Advisor
    Herrmann, Jeffrey W.
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    Abstract
    This paper describes a neural network-based design similarity measure for a variant fixture planning approach. The goal is to retrieve, for a new product design, a useful fixture from a given set of existing designs and their fixtures. However, since calculating each fixture feasibility and then determining the necessary modifications for infeasible fixtures would require too much effort, the approach searches quickly for the most promising fixtures. The proposed approach uses a design similarity measure to find existing designs that are likely to have useful fixtures. The use of neural networks to generate design similarity measures is explored.This paper describes the back-propagation algorithm for network learning and highlights some of the implementation details involved. The neural network-based design similarity measure is compared against other measures that are based on a single design attribute.
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    http://hdl.handle.net/1903/6022
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