The elevated-temperature deformation behavior of Ti2AlNb superalloy was observed by isothermal compression experiments in a wide range of temperatures (950–1200 °C) and strain rates (0.001–10 \({\mathrm{s}}^{-1}\)). The flow behavior is nonlinear, strongly coupled, and multivariable. The constitutive models, namely the double multivariate nonlinear regression model, artificial neural network model, and modified artificial neural network model with an explicit expression, were applied to describe the Ti2AlNb superalloy plastic deformation behavior. The comparative predictability of those constitutive models was further evaluated by considering the correlation coefficient and average absolute relative error. The comparative results show that the modified artificial network model can describe the flow stress of Ti2AlNb superalloy more accurately than the other developed constitutive models. The explicit expression obtained from the modified artificial neural network model can be directly used for finite element simulation. The modified artificial neural network model solves the problems that the double multivariate nonlinear regression model cannot describe the nonlinear, strongly coupled, and multivariable flow behavior of Ti2AlNb superalloy accurately, and the artificial neural network model cannot be embedded into the finite element software directly. However, the modified artificial neural network model is mainly dependent on the quantity of high-quality experimental data and characteristic variables, and the modified artificial neural network model has not physical meanings. Besides, the processing maps were applied to obtain the optimum processing parameters.

Ti2AlNb高温合金的高温变形行为通过等温压缩实验在很宽的温度范围（950–1200°C）和应变率（0.001–10 \（{\ mathrm {s}} ^ {-1} \ ））。流动行为是非线性的，强耦合的并且是多变量的。采用本构模型，即双重多元非线性回归模型，人工神经网络模型和具有明确表示的改进人工神经网络模型，对Ti2AlNb高温合金的塑性变形行为进行了描述。通过考虑相关系数和平均绝对相对误差，进一步评估了这些本构模型的比较可预测性。比较结果表明，改进的人工网络模型可以比其他已开发的本构模型更准确地描述Ti2AlNb高温合金的流动应力。从改进的人工神经网络模型获得的显式表达式可直接用于有限元模拟。改进的人工神经网络模型解决了双重多元非线性回归模型无法准确描述Ti2AlNb高温合金的非线性，强耦合和多变量流动行为的问题，并且人工神经网络模型无法直接嵌入到有限元软件中。但是，改进的人工神经网络模型主要取决于高质量的实验数据和特征变量，而改进的人工神经网络模型没有物理意义。此外，应用加工图以获得最佳加工参数。人工神经网络模型不能直接嵌入到有限元软件中。但是，改进的人工神经网络模型主要取决于高质量的实验数据和特征变量，而改进的人工神经网络模型没有物理意义。此外，应用加工图以获得最佳加工参数。人工神经网络模型不能直接嵌入到有限元软件中。但是，改进的人工神经网络模型主要取决于高质量的实验数据和特征变量，而改进的人工神经网络模型没有物理意义。此外，应用加工图以获得最佳加工参数。