Prediction of Deflection and Stresses of Laminated Composite Plate with an Artificial Neural Network Aid

This paper discusses the use of D-optimal designs in the design of experiments (DOE) and artificial neural networks (ANN) in predicting the deflection and stresses of carbon fibre reinforced plastic (CFRP) square laminated composite plate subjected to uniformly distributed load. For training and testing of the ANN model, a number of finite element analyses have been carried out using D-optimal designs by varying the fibre orientations and thickness of each lamina. The composite plate is modeled using shell 99 elements. The ANN model has been developed using multilayer perceptron (MLP) backpropagation algorithm. The adequacy of the developed model is verified by root mean square error and regression coefficient. The results showed that the training algorithm of backpropagation was sufficient enough in predicting the deflection and stresses.