A method to detect single and multiple delamination problems using a combined neural network technique and genetic algorithm optimization

This thesis develops a new method to detect delaminations in composite laminates using a combination of finite element method, artificial neural networks, and genetic algorithms. Next, this newly developed method is applied to successfully solve delamination detection problems.; Delaminations in a composite laminate with various sizes and locations are considered in the present studies. The improved layerwise shear deformation theory is implemented into the finite element method and used to calculate responses of laminates with single and multiple delaminations. Mappings between the natural frequencies and delamination characteristics are first determined from the developed models. These data are then used to train artificial neural networks of multiplayer perceptron using back-propagation. These trained artificial neural networks are in turn used as an approximate tool to calculate the responses of the delaminated laminates and to feed the data to the delamination detection process. Two different approaches for handling the neural network models are applied in the work and are presented for comparison. The delamination detection problem is formulated as an optimization problem with mixed type design variables. A genetic algorithm, which is a guided probabilistic search technique based on the simulation of Darwin's principle of evolution and natural selection, is developed to solve this optimization problem.; Single through-the-width delamination, single internal delamination, and multiple through-the-width delaminations are separately considered for detection study. At last, the application is extended to the most challenging problem, which is the detection of general delamination. Various factors affecting the detection process such as the finite element convergence factor and the laminate geometry factor are also examined. Case studies are made and the findings are summarized in detail in each chapter of the dissertation.; It is found that the newly developed method successfully detects delaminations with remarkable accuracy for all four types of delamination problems. It is also found that this newly developed method successfully solves the inverse problems of single delamination, internal delamination, multiple delaminations, and the generalized delamination detection, none of which have been solved previously.