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首页> 外文期刊>Journal of Materials Engineering and Performance >Constitutive Modeling of High-Temperature Flow Stress of Armor Steel in Ballistic Applications: A Comparative Study
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Constitutive Modeling of High-Temperature Flow Stress of Armor Steel in Ballistic Applications: A Comparative Study

机译：弹道应用中铠装钢高温流应力的本构模拟：比较研究

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摘要

Armox 500T is one of the armor grade steels extensively used as armor against bullet penetration in ballistic applications. In such applications, the material undergoes plastic deformation at large strain rates (10(3) s(-1)) at temperatures of the order of 673-1373 K. In the present work, an attempt has been made to predict strain rate and temperature-dependent flow behavior of Armox 500T steel through physical-based model like modified Zerilli-Armstrong (M-ZA) and phenomenological-based models like Cowper Symonds (CS), modified Johnson-Cook (M-JC), Arrhenius (Arr.) and Khan-Huang-Liang (KHL) constitutive material model. Isothermal uniaxial compression tests at low strain rates (10(-3)-10(-1) s(-1)) and dynamic compression tests at high strain rates (600-3000 s(-1)) in the temperatures range of 673-1373 K have been carried out to determine constitutive material model parameters. In addition, an artificial neural network (ANN) model that works on multilayer perceptron (MLP) based back propagation neural network (BPNN) has also been developed. The results from all these models have been compared in terms of three statistical parameters namely correlation coefficient (R), mean absolute percent error (MAPE) and its standard deviation (Delta). The results revealed that M-JC model shows highest correlation coefficient and lowest average absolute error. Further, ANN model prediction has the highest accuracy with R = 0.999 and MAPE = 1.052.
机译：Armox 500T是广泛用作防弹的护甲秤之一，防止弹道应用中的子弹渗透。在这种应用中，该材料在673-1373k的温度下以大应变速率（10（3）（-1））以673-1373k的温度经历塑性变形。在本作工作中，已经尝试预测应变率和通过基于物理的模型，如改性Zerilli-Armstrong（M-ZA）和基于现象学的模型，如Cowper Symonds（CS），改装了Johnson-Cook（M-JC），Arrhenius（Arr。 ）和Khan-Huang-Liang（KHL）组成材料模型。低应变速率的等温单轴压缩试验（10（3）-10（-1）S（-1））和高应变速率的动态压缩试验（600-3000秒（-1））在673的温度范围内已经进行了-1373 k以确定组成型材料模型参数。另外，还开发了一种人工神经网络（ANN）模型，其基于多层Perceptron（MLP）的回传播神经网络（BPNN）。在三个统计参数中，已经将所有这些模型的结果进行了比较，即相关系数（R），平均绝对百分比误差（MAPE）及其标准偏差（DELTA）。结果表明，M-JC模型显示最高的相关系数和最低平均绝对误差。此外，ANN模型预测具有最高精度，r = 0.999和mape = 1.052。

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