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.

中文翻译：

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高速冲击载荷下杂化纤维增强聚合物复合材料的能量吸收研究

摘要 本文研究了Kevlar 和玻璃层的杂化碳纤维增强聚合物(CFRP) 复合材料在高速冲击载荷下的能量吸收能力。所开发的方法基于细观宏观尺度有限元建模、实验测试和利用依赖人工神经网络的预测算法。通过对复合材料层压板的纱线和基体采用适当的失效标准，在数值模型中考虑了诱导损伤机制，并根据实验结果验证了结果。基于人工神经网络的算法用于优化混合碳纤维复合材料的能量吸收能力。已经发现，通过考虑适当的层压板叠层而不显着增加层压板质量，可以实现能量吸收的显着更高的改进。CFRP 层压板的最佳混合导致吸收能量提高了 135%，而层压板的质量仅增加了 9%。这项研究可以为设计和分析高速冲击载荷下的混合复合材料层压板提供一种可靠且具有成本效益的方法。