Thick Composite Beam Model for Delamination Prediction Using Neural Networks
Delamination in composite structures is of interest because it can cause catastrophic failure. A consequence of delamination is the change in stiffness of the structure. This effects the modal frequencies of the structure. The applicability of neural networks in determining delaminations in laminated composite beams is examined. A composite beam model based on a shear deformation theory is developed to predict the natural frequencies. The beam model developed is applicable to various boundary conditions. A back propagation neural network is trained to predict the delamination size and location from the natural frequencies of the beam. The neural network model is found to be quite successful in determining the delamination size and location.
M. T. Valoor and K. Chandrashekhara, "Thick Composite Beam Model for Delamination Prediction Using Neural Networks," Intelligent Engineering Systems Through Artificial Neural Networks, American Society of Mechanical Engineers (ASME), Jan 1997.
1997 Artificial Neural Networks in Engineering Conference
Mechanical and Aerospace Engineering
Keywords and Phrases
Backpropogation; Composite Beams and Girders; Composite Structures; Delamination; Mathematical Models; Shear Deformation; Stiffness
Article - Conference proceedings
© 1997 American Society of Mechanical Engineers (ASME), All rights reserved.
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