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.
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Andersen, K., Cook, G. E., Karsai, G. and Ramaswamy, K. (1990) Artificial neural networks applied to arc welding process modeling and control. IEEE Transactions on Industry Applications, 26, 824–830.
ASME (1990) Vitreous china plumbing fixtures, ASME A112.19.2M-1990.
Baffes, P. T. (1989) NETS User's Guide, Version 2.0, Lyndon B. Johnson Space Center.
Cariapa, V., Akbay, K. S. and Rudraraju, R. (1991) Application of neural networks for compliant tool polishing operation, in Computer-Aided Production Engineering, Venkatesh, V. and McGeough, J. (eds), Elsevier Science Publishers, Amsterdam, pp. 271–280.
Chryssolouris, G., Lee, M., Pierce, J. and Domroese, M. (1989) Use of neural networks for the design of manufacturing systems, in Proceedings of the Winter Annual Meeting of the American Society of Mechanical Engineers, Chao, N.-H. and Lu, S. C.-Y. (eds), ASME, New York, pp. 57–63.
Consumer Reports (1990) How to save water. Consumer Reports, 465–466.
Ho, Tuan T., Ho, Hai T. and Bialasiewicz, J. T. (1991) Stochastic neural adaptive control for nonlinear time varying systems, in Intelligent Engineering Systems Through Artificial Neural Networks, Dagli, C., Kumara, S. and Shin, Y. (eds), ASME Press, New York, pp. 531–536.
Hoskins, D. A., Hwang, J. N. and Vagners, J. (1992) Iterative inversion of neural networks and its application to adaptive control. IEEE Transactions on Neural Networks, 3, 292–301.
Hung, S. L. and Adeli, H. (1991) A model of perceptron learning with a hidden layer for engineering design. Neurocomputing, 3, 3–14.
Hwang, Jenq-Neng, Chan, Chi-Hou and Marks, R. J. II (1990) Frequency selective surface design based on iterative inversion of neural networks, in Proceedings of the International Joint Conference on Neural Networks, IEEE, New York, I-39–44.
Kinney, B. H. (1977) The Eljer Caster Manual, Eljer Industries, pp. 34–37.
Lambert, J.-M. and Hecht-Nielsen, R. (1991) Application of feedforward and recurrent neural networks to chemical plant predictive modeling, in Proceedings of the International Joint Conference on Neural Networks, IEEE, New York, I-373–378.
Lawrence, J. (1991) Data preparation for a neural network. AI Expert, 6, 34–41.
Liu, Lihsin and Liu, G. H. (1990) Neural network computing applied in design for manufacturability, in Proceedings of the IEEE/SEMI International Semiconductor Manufacturing Science Symposium, IEEE, New York, pp. 107–110.
Muller, B. and Reinhardt, J. (1990) Neural Networks: An Introduction, Springer-Verlag, Berlin, pp. 62–70.
Okafor, A. C., Marcus, M. and Tipirneni, R. (1990) Multiple sensor integration via neural networks for estimating surface roughness and bore tolerance in circular end milling, in 1990 itTransactions of NAMRI/SME, SME, Dearborn, MI, pp. 128–137.
Rumelhart, D. E., McClelland, J. L. and the PDP Research Group (1986) Parallel Distributed Processing: Exploration in the Microstructure of Cognition, Vol. 1, MIT Press, Cambridge, MA, pp. 318–362.
Schmerr, L. W., Nugen, S. M. and Forourachi, B. (1991) Planning robust design experiments using neural networks and Taguchi methods, in Intelligent Engineering Systems Through Artificial Neural Networks, Dagli, C., Kumara, S. and Shin, Y. (eds), ASME Press, New York, pp. 829–834.
Sejnowski, T. J. and Rosenberg, C. R. (1987) Parallel networks that learn to pronounce English text. Complex Systems, 1, 145–168.
Smith, A. E. (1991) Quality control using backpropagation: an injection molding application, in Intelligent Engineering Systems Through Artificial Neural Networks, Dagli, C., Kumara, S. and Shin, Y. (eds), ASME Press, New York, pp. 729–734.
Steck, J., Krishnamurthy, K., McMillin, B. and Leininger, G. (1991) Neural modeling and control of a distillation column, in Proceedings of the 1991 Joint Conference on Neural Networks, IEEE, New York, II-771–774.
Werbos, P. J. (1988) Generalization of backpropagation with application to a recurrent gas market model. Neural Networks, 1, 339–356.
Zarefar, H. and Goulding, J. R. (1992) Neural networks in design of products: a case study, in Intelligent Design and Manufacturing, Kusik, A. (ed.), John Wiley & Sons, New York, pp. 179–201.
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Ben Brahim, S., Smith, A.E. & Bidanda, B. Relating product specifications and performance data with a neural network model for design improvement. J Intell Manuf 4, 367–374 (1993). https://doi.org/10.1007/BF00123950
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DOI: https://doi.org/10.1007/BF00123950