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Machine learning in configuration design

Published online by Cambridge University Press:  27 February 2009

Tim Murdoch  and
Nigel Ball
Tim Murdoch
Affiliation:
Engineering Design Centre, Cambridge University Engineering Department, Trumpington Street, Cambridge, CB2 1PZ, U.K.
Nigel Ball
Affiliation:
Engineering Design Centre, Cambridge University Engineering Department, Trumpington Street, Cambridge, CB2 1PZ, U.K.
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Abstract

New methods of configuration analysis have recently emerged that are based on development trends characteristic of many technical systems. It has been found that though the development of any system aims to increase a combination of the performance, reliability and economy, actual design changes are frequently kept to a minimum to reduce the risk of failure. However, a strategy of risk reduction commits the designer to an existing configuration and an approved set of components and materials. Therefore, it is important to analyze the configurations, components, and materials of past designs so that good aspects may be reused and poor ones changed. A good configuration produces the required performance and reliability with maximum economy. These three evaluation criteria form the core of a configuration optimization tool called KATE, where known configurations are optimized producing a set of ranked trial solutions. The authors suggest that this solution set contains valuable design knowledge that can be reused. This paper briefly introduces a generic method of configuration evaluation and then describes the use of a self-organizing neural network, the Kohonen Feature Map, to analyze solution sets by performing an initial data reduction step, producing archetype solutions, and supporting qualitative clustering.

Keywords

Configuration OptimizationKohonen Feature Map
Type
Articles
Information
AI EDAM , Volume 10 , Issue 2: Special Issue: Machine learning in design , April 1996 , pp. 101 - 113
Copyright
Copyright © Cambridge University Press 1996

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References

REFERENCES

Ball, N.R., & Murdoch, T.N.S. (1994). Analysing design configuration spaces using a self-organising neural network. Adaptive Computing in Engineering Design and Control – '94, 90–96.Google Scholar
Byworth, S. (1987). Design and development of high temperature turbines. Turbomachinery Int. May/June.Google Scholar
Caudell, T.P., Smith, D.G., Escobedo, R., & Anderson, M. (1994). NIRS: Large scale ART-1 neural architectures for engineering design retrieval. Neural Networks 7(9), 1339–1350.CrossRefGoogle Scholar
Coplin, J.F. (1989). Engineering design – A powerful influence on the business success of manufacturing industry. ICED-89, 1–32.Google Scholar
Duffy, A.H.B., & Kerr, S.M. (1993). Customised perspectives of past designs from automated group rationalisations. Artificial Intelligence in Engineering, 8, 183–200.CrossRefGoogle Scholar
Koh, J., Suk, M., & Bhandarkar, S.M. (1995). A multilayer self-organizing feature map for range image segmentation. Neural Networks 8(1), 67–86.CrossRefGoogle Scholar
Kohonen, T. (1984). Self-organisation and associative memory. Springer-Verlag, New York.Google Scholar
Kohonen, T., Torkkola, K., Shozakai, M., Kangas, J., & Vent, O. (1987). Microprocessor implementation of a large vocabulary speech recognizer and phonetic typewriter for Finnish and Japanese. Proc. Eur. Conf. Speech Technol. 377–380.CrossRefGoogle Scholar
Martinelli, G., Ricotti, L.P., & Ragazzini, S. (1990). Nonstationary lattice quantization by a self-organizing neural network. Neural Networks 3(4), 385–393.CrossRefGoogle Scholar
Matsuyama, Y. (1988). Vector quantization with optimized grouping and parallel distributed processing. Neural Networks 1, 36–42.Google Scholar
Murdoch, T.N.S. (1994). Machine learning in configuration design: A case study in configuration analysis. Workshop on Machine Learning in Design, AID-94.Google Scholar
Murdoch, T.N.S. (1993). Configuration evaluation and optimisation of technical systems. Ph.D. Thesis, Cambridge University.Google Scholar
Murdoch, T.N.S., & Wallace, K.M. (1992). Configuration optimisation of technical systems. J. Eng. Design 3(2), 99–116.CrossRefGoogle Scholar
Murdoch, T.N.S., & Wallace, K.M. (1995). Design for technical merit, design for X: Concurrent engineering imperatives. Chapman & Hall, New York.Google Scholar
Pahl, G., & Beitz, W. (1984). Engineering design. Design Council, London.Google Scholar
Reich, Y., Konda, S.L., Levy, S.N., Monarch, I.A., & Subrahmanian, E. (1993). New roles for machine learning in design. Artif. Intell. Eng. 8(3), 165–181.CrossRefGoogle Scholar
Schraudolph, N. (1990). A user’s guide to GENESIS 1.2ucsd. University of California, San Diego, CA.Google Scholar
Statnikov, R.B., & Matusov, J.B. (1995). Multicriteria optimization and engineering. Chapman & Hall, New York.CrossRefGoogle Scholar
Vercauteren, L., Sieben, G., Praet, M., Otte, G., Vingerhoeds, R., Boullart, L., Calliauw, L., & Roels, H. (1990). The classification of brain tumours by a topological map. Proc. Int. Conf. Neural Nets., 387–391.Google Scholar