In the present work, E-glass/polyester composite laminates were manufactured in a customized resin transfer mould (RTM) with different layers of fiber at selected resin injection pressures. Experiments were performed employing full factorial design to study the influence of number of fiber layers and resin injection pressure on mechanical properties of the composites. Analysis of variance was implemented to study the interaction effect of process parameters on multi-responses namely tensile, flexural and impact strengths. Taguchi method based grey relational analysis was used to determine optimal control factors for the responses. Numbers of fiber layers and the injection pressure have significance with respective 73.96% and 16.57% contributions on the grey relation grades of the three responses. An optimal working condition was suggested to produce quality composite. In addition, mathematical models for the mechanical properties were also developed using the experimental results.

Flow front velocity of resin plays a key role in yielding better impregnation of fiber reinforcement in resin transfer molding to fabricate quality composites. A video camera was used to capture the patterns of resin flowing below the grid lines drawn on the acrylic sheet of resin transfer mold and also to accurately measure the flow front velocity using an image conversion tool. The effect of flow front velocity, permeability, Reynolds number and void content at five different injection pressures on chopped strand E-glass/polyester composites consisting of 4, 5 and 6 layers has been studied. On the basis of Reynolds number of resin flow and void content present in the composite, optimal injection pressures are suggested for better impregnation of preform. Composites processed at these injection pressures gave superior properties in tension and flexure. Fractured parts of the specimens were examined on Scanning Electron Microscope to explain the causes of superior mechanical properties.

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.Keywords Artificial neural network (ANN) · Teaching learning based optimization (TLBO) · Glass fiber reinforced plastic (GFRP) · Resin transfer molding (RTM) · Mechanical properties