SiCp/2014Al复合材料的热变形行为及本构模型

在Gleeble-3180热模拟机上对碳化硅颗粒增强铝基(SiCp/2014Al)复合材料进行热压缩试验，研究其在变形温度为350，400，450 ℃和500 ℃，应变速率为0.001，0.01，0.1s-1和1.0 s-1条件下的热变形行为。根据热压缩实验的真应变-真应力数据，在考虑应变、应变速率和变形温度对流动应力的耦合影响下构建修正的Johnson-Cook(JC)本构模型，同时建立人工神经网络模型(ANN)。结果表明:SiCp/2014Al复合材料的流变应力随应变速率的增加和温度的降低而增大。与修正的JC模型相比，ANN模型具有较低的均方根误差(0.51 MPa)和平均绝对误差(1.43%)，以及较高的相关系数(0.999 7)，表明其对SiCp/2014Al复合材料热变形流变应力的预测具有更高的预测精度和可靠性。 

On the hot deformation behavior and constitutive model of SiCp/2014Al composites

The hot deformation behavior of silicon carbide particles reinforced aluminum matrix composite (SiCp/Al) was investigated by isothermal compression testing at temperatures of 350 ℃, 400 ℃, 450 ℃, and 450 ℃ as well as strain rates of 0.001~1 s-1 on a Gleeble-3180 simulator. Based on the true strain-true stress data of hot compression experiment, the modified Johnson-Cook constitutive model (M-JC) was constructed, considering the coupling effects of strain, strain rate and deformation temperature on flow stress, and the Artificial neuron network models (ANN) was established. The results show that the flow stress of SiCp/2014Al composite increases with the increase of stain rate and the decrease of temperature. Compared with the M-JC model, the ANN model has a lower root mean square error of 0.51 MPa, a lower average absolute error of 1.43%, and a higher correlation coefficient (0.999 7), which indicates that the ANN that it has higher prediction accuracy and reliability in the prediction of the hot deformation behavior of SiCp/2014Al composite.