基于变形温度250～400℃和应变速率0.001～1 s-1条件下的铸态AZ80镁合金的热压缩试验数据,建立了基于应力位错关系和动态再结晶动力学的物理基本构模型以及前馈反向传播算法的人工神经网络(ANN)模型来预测AZ80镁合金的热变形行为.采用相关系数(R)、平均绝对相对误差(AARE)、相对误差(RE)3种统计学指标来验证2种模型的预测精度.结果 表明,2种模型均可以准确预测AZ80镁合金的热变形行为.其中,ANN模型预测的应力值与实验数据更为吻合,其R和AARE分别为0.9991和2.02％,而物理基本构模型预测的R和AARE分别为0.9936和4.52％.ANN模型较好的预测能力归功于它擅长处理复杂的非线性关系,而物理基本构模型的预测能力是基于模型具有一定的物理意义,模型参数的确定充分考虑了热变形过程中的加工硬化(WH)、动态回复(DRV)和动态再结晶(DRX)的热动力学机制.最后,对这2种本构模型的优缺点及适用范围进行了比较讨论.“,”Based on the hot compression test data of as-cast AZ80 magnesium alloy under the conditions of deformation temperature of 250～400 ° C and strain rate of 0.001～1 s-1,a physical-based constitutive model based on the stress dislocation correlation and dynamic recrystallization dynamics and an artificial neural network (ANN) model based on feedforward backpropagation algorithm were established to predict the thermal deformation behavior of AZ80 magnesium alloy.Three statistical indicators,correlation coefficient (R),mean absolute relative error (AARE),and relative error (RE),were used to verify the prediction accuracy of these two models.The results show that both the models can accurately predict the thermal deformation behavior of AZ80 magnesium alloy.The stress value predicted by ANN model shows better agreement with the experimental data,and the value of R and AARE of ANN model is 0.9991 and 2.02％,respectively.While the R and AARE predicted by the physical-based constitutive model are 0.9936 and 4.52％,respectively.The better predictive ability of ANN model is attributed to its ability to deal with complex nonlinear relationships,while the predictive ability of the physical-based constitutive model is attributed to the fact that the model has certain physical meaning.The thermodynamic mechanism of work hardening (WH),dynamic recovery (DRV),and dynamic recrystallization(DRX) during thermal deformation are fully considered in the model parameters.Finally,the advantages and disadvantages of these two models are compared and discussed.