Title: Design and degradation modelling through artificial neural networks

Authors: Hungyen Lin, L.X. Kong, Hung-Yao Hsu

Addresses: Centre for Advanced Manufacturing Research, University of South Australia, Mawson Lakes SA 5095, Australia. ' Centre for Advanced Manufacturing Research, University of South Australia, Mawson Lakes SA 5095, Australia. ' Centre for Advanced Manufacturing Research, University of South Australia, Mawson Lakes SA 5095, Australia

Abstract: 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.

Keywords: degradation data; degradation paths; accelerated degradation testing; artificial neural networks; ANNs; Australia; automobile industry; reliability testing; vehicle design; automotive components; design evaluation; product development; folding force degradation; component design.

DOI: 10.1504/IJMR.2007.013429

International Journal of Manufacturing Research, 2007 Vol.2 No.1, pp.97 - 113

Available online: 27 Apr 2007 *

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