Using Artificial Neural Networks to Predict the Fatigue Life of Different Composite Materials Including the Stress Ratio Effect

2010 ◽  
Vol 18 (4) ◽  
pp. 297-309 ◽  
Author(s):  
Mohamed Al-Assadi ◽  
Hany A. El Kadi ◽  
Ibrahim M. Deiab
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fatigue Life ◽  
Composite Materials ◽  
Stress Ratio ◽  
Ratio Effect ◽  
Artificial Neural

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

2011 ◽  
Vol 471-472 ◽  
pp. 221-226 ◽  
Author(s):  
Hany A. El Kadi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fatigue Life ◽  
Composite Materials ◽  
Life Prediction ◽  
Fatigue Behavior ◽  
Fatigue Life Prediction ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
Polynomial Classifiers

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.

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