Fault detection of laminated composite plate with delamination damage using neural network training based on the free vibration response

In this study, error back-propagation neural network is used for fault detection of composite plate with delamination damage. At the first step of the fault detection process, a free vibration analysis of laminated composite plates is performed based on numerical finite element method and the natural frequencies of individual modes is obtained for different delamination models (size, geometry and location of the delamination region). Then natural frequencies extracted from the model are considered as the input parameter and the size, geometry and location of the delamination region are also considered as the output parameters of the neural network. 8-layers composite plate is modeled based on the three-dimensional elasticity theory and considering hexagonal brick elements. So, transverse shear deformations effect is taken into account in the modeling of composite plate. ABAQUS software capabilities are used for modeling because of the complexity of process governing on the composite plate with delamination. The numerical results obtained by the finite element method are compared and validated with available numerical and experimental data. Two training methods including Levenberg - Marquardt and Error propagation flexible algorithm are used to train the neural network and compare responses. Predicted results by Levenberg – Marquardt training method are in very good agreement with the values obtained by the finite element method. After training the neural network, the model generalization is used for predicting and detecting of the damage in composite plate.