Modeling of glass fiber reinforced composites for optimal mechanical properties using teaching learning based optimization and artificial neural networks

Abstract

The present work is aimed at determining mechanical properties of chopped strand glass fiber reinforced composite laminates manufactured based on the design of experiments by resin transfer molding at various injection pressures with 4, 5 and 6 layers. Response surface methodology was implemented to the experimental data for evaluating the effect of number of layers and resin injection pressure on mechanical properties and void content. Teaching learning based optimization (TLBO) has been proposed to predict optimal (maximum) mechanical properties of composite by optimizing the number of layers and injection pressure. Artificial neural network (ANN) with feed forward back propagation algorithm was also used to predict the responses and compare with experimental and TLBO results. It was found that the predicted values of responses from TLBO and ANN are good in agreement with experimental results.