-1條件下的鑄態(tài)AZ80鎂合金的熱壓縮試驗(yàn)數(shù)據(jù)，建立了基于應(yīng)力位錯關(guān)系和動態(tài)再結(jié)晶動力學(xué)的物理基本構(gòu)模型以及前饋反向傳播算法的人工神經(jīng)網(wǎng)絡(luò)（ANN）模型來預(yù)測AZ80鎂合金的熱變形行為。采用相關(guān)系數(shù)（R）、平均絕對相對誤差（AARE）、相對誤差（RE）3種統(tǒng)計學(xué)指標(biāo)來驗(yàn)證2種模型的預(yù)測精度。結(jié)果表明，2種模型均可以準(zhǔn)確預(yù)測AZ80鎂合金的熱變形行為。其中，ANN模型預(yù)測的應(yīng)力值與實(shí)驗(yàn)數(shù)據(jù)更為吻合，其R和AARE分別為0.9991和2.02%，而物理基本構(gòu)模型預(yù)測的R和AARE分別為0.9936和4.52%。ANN模型較好的預(yù)測能力歸功于它擅長處理復(fù)雜的非線性關(guān)系，而物理基本構(gòu)模型的預(yù)測能力是基于模型具有一定的物理意義，模型參數(shù)的確定充分考慮了熱變形過程中的加工硬化（WH）、動態(tài)回復(fù)（DRV）和動態(tài)再結(jié)晶（DRX）的熱動力學(xué)機(jī)制。最后，對這2種本構(gòu)模型的優(yōu)缺點(diǎn)及適用范圍進(jìn)行了比較討論。;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."/>