应用本构模型和神经网络模型预测铝/镁基纳米复合材料的高温流变行为(英文)

为了预测Al/Mg基纳米复合材料的高温流变行为,在不同的应变速率(0.01-1.0s-)和温度(523,623和1723K)的条件下进行热压缩试验,利用所得到的应力-应变数据,开发了本构模型,比如一般流动方程。阿累尼乌斯双曲模型、Johnson-Cook(JC)和改性的Zerilli-Armstrong(ZA)模型及人工神经网络(ANN)模型。通过使用统计参数,例如均方根误差(RMSE)、回归系数(R2)、平均相对误差(MRE)和分散指数(Is),比较了人工神经网络和不同的本构模型。结果表明,人工神经网络模型对AA5083-2%TiC复合材料的热变形流动应力的评估准确性更高。

Application of constitutive and neural network models for prediction of high temperature flow behavior of Al/Mg based nanocomposite

To predicate the high temperature flow behavior of Al/Mg based nanocomposite, constitutive models such as general flow, Arrhenius hyperbolic, Johnson-Cook(JC) and modified Zerilli-Armstrong (ZA) models, and artificial neural network(ANN) models were developed using stress-strain data collected from hot compression tests carried at different strain rates (0.01-1.0 s^?1) and temperatures (523, 623 and 723 K). The validity of the models developed was tested using statistical parameters such as root mean square error (RMSE), regression coefficient (R²), mean relative error (MRE) and scattered index (I_s). A comparison between ANN and different constitutive models shows that the ANN model has a higher accuracy in estimating the flow stress during hot deformation of AA5083/2%TiC nanocomposite.