TY - GEN

T1 - Delamination detection problems using a combined genetic algorithm and neural network technique

AU - Chen, H. P.

AU - Le, Hieu

AU - Kim, Jin

AU - Chattopadhyay, Aditi

PY - 2004

Y1 - 2004

N2 - The delamination detection problem is formulated as an optimization problem with mixed type design variables using genetic algorithms. Natural frequency is taken as the global damage index for detection of delamination in composite laminates. 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 pattern. 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 configurations - through-the-width delamination and internal delamination, have been considered in the present studies. A new modular approach has been developed in constructing the backpropagation neural networks for the internal delamination problem. In this approach, instead of a single backpropagation neural network, multiple smaller size module backpropagation neural networks are used to simulate the vibration modal response. Results with satisfactory accuracy have been obtained in detecting the internal delamination using this technique.

AB - The delamination detection problem is formulated as an optimization problem with mixed type design variables using genetic algorithms. Natural frequency is taken as the global damage index for detection of delamination in composite laminates. 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 pattern. 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 configurations - through-the-width delamination and internal delamination, have been considered in the present studies. A new modular approach has been developed in constructing the backpropagation neural networks for the internal delamination problem. In this approach, instead of a single backpropagation neural network, multiple smaller size module backpropagation neural networks are used to simulate the vibration modal response. Results with satisfactory accuracy have been obtained in detecting the internal delamination using this technique.

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M3 - Conference contribution

AN - SCOPUS:20344372426

SN - 1563477165

SN - 9781563477164

T3 - Collection of Technical Papers - 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

SP - 1098

EP - 1111

BT - Collection of Technical Papers - 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

T2 - Collection of Technical Papers - 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

Y2 - 30 August 2004 through 1 September 2004

ER -