Abstract
In this paper, the energy absorption capability of hybridized carbon fiber-reinforced polymer (CFRP) composites with Kevlar and glass layers subjected to high-velocity impact loading was studied. The developed methodology was based on meso‑macro scale finite element modeling, experimental testing and utilizing a predictive algorithm relying on artificial neural network. The induced damage mechanisms were considered in the numerical model by employing appropriate failure criteria for the yarns and matrix of the composite laminates and the results were validated against experimental results. An artificial neural network-based algorithm was used to optimize the energy absorption capability of the hybridized CFRP composites. It was found out that a considerably higher improvement in the energy absorption can be achieved by considering an appropriate laminate layup without a considerable increase in the laminate mass. The optimum hybridization of CFRP laminates resulted in 135% improvement in the absorbed energy with only 9% increase in the mass of the laminate. This study can provide a reliable and cost-effective method for designing and analyzing hybrid composite laminates under high velocity-impact loading.
Published Version
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