Using Neural Networks to Generate Design Similarity Measures for Variant Fixture Planning

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’s 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 backpropagation 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.