Flexural Characterization of Foam Cored E-Glass Reinforced Smart Materials Using Artificial Neural Network

Abstract The work aims at the comparison of experimental and artificial neural network (ANN) results of smart structures (SWC’s) comprising of three different densities of polyurethane foam (PUF) cored Vinyl ester (VE) SWC’s. As the expected life span of the SWC’s is not properly known yet, the long term durability assessment explores the decay of structural strength of SWC’s. The paper reports the degradation results of PUF cored E-glass mats, i.e., Chopped Strand Stitched Mat (CSM-S) and Stitch Bond Mat (SBM) reinforcement in VE matrix with change in density of PUF for moisture absorption at different temperatures with 95% RH for 180 days. The specimens showed reduced core shear and face bending strength. The experimental results verified using ANN technique with the help of MAT Lab. It is observed that the neural network models are powerful methods for solving the material system issues by determining the complex and nonlinear relationship representing input/output information acquired in the analysis. The observed results infer that the experimental and ANN results are in good convergence