Prediction of impact performance of fiber reinforced polymer composites using finite element analysis and artificial neural network

In this study, a methodology combining finite element analysis (FEA) and artificial neural network (ANN) through multilayer perceptron architecture was utilized to predict the impact resistance behavior of hybrid and non-hybrid fabric reinforced polymer (FRP) composites. A projectile at 250 m s⁻¹ impact velocity was considered for the high velocity impact simulations. The Kevlar, carbon and glass fabric-based epoxy composites were modelled and the impact tests were performed through finite element simulations. The residual velocity results from FEA were used as training data for the ANN prediction. The ANN predicted results were in good agreement with FEA results with a maximum variation of about 6.6%. In terms of impact resistance, composite laminates with more Kevlar layers exhibited enhanced performance compared to other samples. Neat Kevlar/epoxy (K/K/K) exhibited the best impact resistance performance in terms of lowest residual velocity and highest energy absorption of 101.84 m s⁻¹ and 222.86 J, respectively. Whereas, neat glass/epoxy (G/G/G) specimens registered the highest projectile residual velocity (165.13 m s⁻¹) and lowest energy absorption (158.99 J) compared to all other specimens. 2-fabric sandwich composite K/G/K exhibited a low residual velocity of 115.27 m s⁻¹ and high energy absorption of 218.53 J, which is the second best among all specimens. Comparatively, the 3-fabric hybrid composites registered intermediate impact resistance results lower than that of Kevlar rich specimens, but significantly higher than neat G/G/G composite, thus, proving the effectiveness of hybridization in enhancement of impact performance compared to neat glass composite. Overall, the chosen methodology yielded significantly accurate results for the prediction of impact behavior of FRP composites.