Multiple and Generalized Delamination Detections Using Neural Network Technique and Genetic Algorithm

* Principal Engineer, AIAA Member † Professor, AIAA Senior Member ‡ Professor, Fellow ASME, Associate Fellow AIAA The delamination detection problem is formulated as an optimization problem with mixed type design variables using genetic algorithms. A recently developed finite element model based on an improved layerwise composite laminate theory is employed to calculate the natural frequencies of laminates with given delamination patterns. Artificial backpropagation neural networks are trained to simulate results from the finite element analysis. These artificial neural networks are chosen as function approximations, which are used to predict natural frequencies of delaminated laminates with satisfactory accuracy. Two different types of delamination detections – multiple through-the-width delamination and generalized delamination are considered in the present studies. Results with satisfactory accuracy have been obtained in detecting both multiple through-the-width delamination and generalized delamination using this technique.