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Fatigue Life Evaluation of Delaminated GFRP Laminates Using Artificial Neural Networks

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Abstract

Delamination between any two plies is an important damage commonly seen in composite structures. It may initiate and grow in the composite laminates for different loading conditions, and it may finally lead to failure of the component under the cyclic loading. Therefore, fatigue life prediction in these structures is important to avoid the effects of delaminations in economic and safety considerations. The usage of artificial neural network (ANN) in estimating fatigue failure in composites with delaminations would be high. In this work, experimental fatigue data were obtained for glass fiber-reinforced composite beams with and without delaminations. The experimental results were used to train and test the neural network. Finally, ANN was found to be accurate tool for fatigue life estimation for composite materials with delaminations as it produces reasonably good fatigue life prediction for delaminated composites.

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Acknowledgements

The authors would like to thank the Management and Principal of PSG college of Technology, India, for their extensive support for carrying out the research.

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Authors and Affiliations

  1. Department of Production Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India

    T. G. Sreekanth, M. Senthilkumar & S. Manikanta Reddy

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  1. T. G. Sreekanth
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  2. M. Senthilkumar
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  3. S. Manikanta Reddy
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Correspondence to T. G. Sreekanth.

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Sreekanth, T.G., Senthilkumar, M. & Reddy, S.M. Fatigue Life Evaluation of Delaminated GFRP Laminates Using Artificial Neural Networks. Trans Indian Inst Met 74, 1439–1445 (2021). https://doi.org/10.1007/s12666-021-02234-5

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  • DOI: https://doi.org/10.1007/s12666-021-02234-5

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