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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

Information

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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