Relating product specifications and performance data with a neural network model for design improvement

This paper presents research resulting in a neural network model relating product design specifications and performance testing results using data from a sanitary ware manufacturer. The main constraint of the work was the limited availability of actual data for neural network training and testing, a situation often found in real situations where a priori product knowledge is limited during the product design phase. The authors used two training techniques, the standard hold back and the leave-k-out, for the neural network model to leverage the sparseness of the data. Neural network results are compared and contrasted to statistical models relating product design and performance. This work is an exploration of the value of neural network models to assist with interactive product design. Revised October 1992 for Journal of Intelligent Manufacturing