Detection and Classification of Impact-Induced Damage in Composite Plates using Neural Networks

Donald C. Wunsch, Missouri University of Science and Technology
K. Chandrashekhara, Missouri University of Science and Technology
Steve Eugene Watkins, Missouri University of Science and Technology
Farhad Akhavan
Rohit Dua, Missouri University of Science and Technology

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Artificial neutral networks (ANN) can be used as an online health monitoring systems (involving damage assessment, fatigue monitoring and delamination detection) for composite structures owing to their inherent fast computing speeds, parallel processing and ability to learn and adapt to the experimental data. The amount of impact-induced strain on a composite structure can be found using strain sensors attached to composite structures. Prior work has shown that strain-based ANN can characterize impact energy on composite plates and that strain signatures can be associated with damage types and severity. This paper reports the extension of this approach for damage classification using finite element analysis to simulate impact-induced strain profiles resulting from impact on composite plates. An ANN employing the backpropagation algorithm was developed to detect and classify this damage