Predicting Fatigue Life of Pre-Corroded LC4 Aluminum Alloy by Artificial Neural Network

Abstract:

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The purpose of this work was to predict the fatigue life of pre-corroded LC4 aluminum alloy by applying artificial neural network (ANN). Specimens were exposed to the same corrosive environment for 24h, 48h, and 72h. Fatigue tests were conducted under different stress levels. The existing experimental data sets were used for training and testing the construction of proposed network. A suitable network architecture (2-15-1) was proposed with good performance in this study. For evaluating the method efficiency, the experimental results have been compared to values predicted by ANN. The maximum absolute relative error for predicted values does not exceed 5%. Therefore it can be concluded that using neural networks to predict the fatigue life of LC4 is feasible and reliable.

Info:

Periodical:

Advanced Materials Research (Volumes 118-120)

Edited by:

L.Y. Xie, M.N. James, Y.X. Zhao and W.X. Qian

Pages:

221-225

DOI:

10.4028/www.scientific.net/AMR.118-120.221

Citation:

C. L. Xu et al., "Predicting Fatigue Life of Pre-Corroded LC4 Aluminum Alloy by Artificial Neural Network", Advanced Materials Research, Vols. 118-120, pp. 221-225, 2010

Online since:

June 2010

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Price:

$35.00

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