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
R. Dua et al., "Detection and Classification of Impact-Induced Damage in Composite Plates using Neural Networks," Proceedings of the International Joint Conference on Neural Networks, 2001, Institute of Electrical and Electronics Engineers (IEEE), Jan 2001.
The definitive version is available at https://doi.org/10.1109/IJCNN.2001.939106
International Joint Conference on Neural Networks, 2001
Electrical and Computer Engineering
Mechanical and Aerospace Engineering
Keywords and Phrases
Backpropagation; Composite Plates; Computerised Monitoring; Damage Detection; Feedforward Neural Nets; Feedforward Neutral Networks; Fibre Reinforced Composites; Finite Element Analysis; Impact-Induced Damages; Materials Testing; Monitoring; Pattern Classification; Real-Time Systems
Article - Conference proceedings
© 2001 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.