Design and degradation modelling through artificial neural networks

Lin, Hungyen, Kong, L. X. and Hsu, Hung-Yao 2007, Design and degradation modelling through artificial neural networks, International journal of manufacturing research, vol. 2, no. 1, pp. 97-113.

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Title Design and degradation modelling through artificial neural networks
Author(s) Lin, Hungyen
Kong, L. X.ORCID iD for Kong, L. X.
Hsu, Hung-Yao
Journal name International journal of manufacturing research
Volume number 2
Issue number 1
Start page 97
End page 113
Publisher Inderscience
Place of publication Olney, England
Publication date 2007
ISSN 1750-0591
Keyword(s) degradation data
degradation path
accelerated degradation testing
Artificial Neural Networks
Summary Automotive is one of the major manufacturing industries in Australia that requires extensive reliability test for the components used in vehicles. To achieve a shorter time-to-market and a highly reliable product while reducing the amount of physical prototyping, there is a growing need for better understanding on the effect that the design parameters have on the degradation of the product. This paper presents comprehensive descriptions of applying Artificial Neural Network (ANN) to capture the relationships between design and degradation. Consequently, two models of different practical significance are created as the result of the work. The vision of the models is to be used by the testers and designers as a guideline in design evaluation, so that time-consuming and expensive iterations of the product developmental cycle can be reduced substantially. The degradation of the folding force of a mechanical system is used to illustrate our approach.
Language eng
Field of Research 080110 Simulation and Modelling
090204 Automotive Safety Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2007, Inderscience Enterprises Ltd.
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