A prediction model for dynamic compressive response of composite materials considering strain rate

doi:10.19936/j.cnki.2096-8000.20240728.003

Abstract

Abstract: This article proposes a dynamic compression response prediction model for composite materials considering strain rate effects. Based on neural networks, a high strain rate compression response prediction model is constructed for carbon fiber reinforced composite materials prepared from T300/QY8911 prepreg with material properties as parameter variables. The dynamic compression response of the material at strain rates of 517 s^-1, 721 s^-1, and 1 070 s^-1 is predicted, and experiments are conducted to verify the prediction results. The results indicate that the predicted results are in good agreement with the experimental results, the maximum error of stress is only -1.585 6%, and the maximum error of the strain is only 1.703 7%.

Key words: carbon fiber reinforced composites, strain rate effect, neural network, split Hopkinson pressure bar, low-velocity impact

CLC Number:

TB332

WEN Jinjun, QU Meijiao, SHEN Yongao, LI Mengqi, SONG Yuheng, ZHU Hanrui. A prediction model for dynamic compressive response of composite materials considering strain rate[J]. COMPOSITES SCIENCE AND ENGINEERING, 2024, 0(7): 23-30.

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