Fatigue Life Prediction of Composite Materials: Artificial Neural Networks vs. Polynomial Classifiers

Abstract:

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Artificial neural networks (ANN) and polynomial classifiers (PC) have been successfully used to predict the fatigue failure of fiber reinforced composite materials. This includes predicting the behavior of the same material subjected to different loading conditions as well as predicting the fatigue behavior of different materials. In this work, the fatigue life prediction obtained using both methods will be compared. The effect of the various parameters influencing the prediction will be presented and the advantages and disadvantages of each of the methods will be discussed.

Info:

Periodical:

Key Engineering Materials (Volumes 471-472)

Edited by:

S.M. Sapuan, F. Mustapha, D.L. Majid, Z. Leman, A.H.M. Ariff, M.K.A. Ariffin, M.Y.M. Zuhri, M.R. Ishak and J. Sahari

Pages:

221-226

DOI:

10.4028/www.scientific.net/KEM.471-472.221

Citation:

H. A. El Kadi "Fatigue Life Prediction of Composite Materials: Artificial Neural Networks vs. Polynomial Classifiers", Key Engineering Materials, Vols. 471-472, pp. 221-226, 2011

Online since:

February 2011

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Price:

$35.00

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