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Predicting the Fatigue Life of Different Composite Materials Using Artificial Neural Networks

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Abstract

Artificial Neural Networks (ANN) have been recently used in modeling the mechanical behavior of fiber-reinforced composite materials including fatigue behavior. The use of ANN in predicting fatigue failure in composites would be of great value if one could predict the failure of materials other than those used for training the network. This would allow developers of new materials to estimate in advance the fatigue properties of their material. In this work, experimental fatigue data obtained for certain fiber-reinforced composite materials is used to predict the cyclic behavior of a composite made of a different material. The effect of the neural network architecture and the training function used were also investigated. In general, ANN provided accurate fatigue life prediction for materials not used in training the network when compared to experimentally measured results.

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Authors and Affiliations

  1. College of Engineering, American University of Sharjah, Sharjah, UAE

    M. Al-Assadi, H. El Kadi & I. M. Deiab

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  1. M. Al-Assadi
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  2. H. El Kadi
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  3. I. M. Deiab
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Correspondence to H. El Kadi.

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Al-Assadi, M., El Kadi, H. & Deiab, I.M. Predicting the Fatigue Life of Different Composite Materials Using Artificial Neural Networks. Appl Compos Mater 17, 1–14 (2010). https://doi.org/10.1007/s10443-009-9090-x

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  • DOI: https://doi.org/10.1007/s10443-009-9090-x

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