Abstract
Artificial Neural Networks (ANN) have been successfully used in predicting the fatigue behavior of fiber-reinforced composite materials. In most cases, the predictions were obtained for the same material used in training subjected to different loading conditions. The method would be of greater value if one could predict the failure of materials other than those used for training the network. In a recent paper, ANN trained using the experimental fatigue data obtained for composites subjected to a constant stress ratio \( \left( {{\hbox{R}} = {\sigma_{{ \min }}}/{\sigma_{{ \max }}}} \right) \) was successfully used to predict the cyclic behavior of a composite made of a different material. In this work, this method is extended to include the stress ratio effect. The results show that ANN can provide accurate fatigue life prediction for different materials under different values of the stress ratio. These results can allow for the development of a materials smart database that can be used for various engineering applications.
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Al-Assadi, M., El Kadi, H.A. & Deiab, I.M. Using Artificial Neural Networks to Predict the Fatigue Life of Different Composite Materials Including the Stress Ratio Effect. Appl Compos Mater 18, 297–309 (2011). https://doi.org/10.1007/s10443-010-9158-7
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DOI: https://doi.org/10.1007/s10443-010-9158-7