Zhao Rui, He Jianchao, Tian Hao, Jing Yongjuan, Xiong Jie
Institute of Special Environment Physical Sciences, Harbin Institute of Technology, Shenzhen 518055, China.
School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen 518055, China.
Materials (Basel). 2023 Jul 13;16(14):4987. doi: 10.3390/ma16144987.
The hot deformation behaviors of a Ti46Al2Cr2Nb alloy were investigated at strain rates of 0.001-0.1 s and temperatures of 910-1060 °C. Under given deformation conditions, the activation energy of the TiAl alloy could be estimated as 319 kJ/mol. The experimental results were predicted by different predictive models including three constitutive models and three data-driven models. The most accurate data-driven model and constitutive model were an artificial neural network (ANN) and an Arrhenius type strain-compensated Sellars (SCS) model, respectively. In addition, the generalization capability of ANN model and SCS model was examined under different deformation conditions. Under known deformation conditions, the ANN model could accurately predict the flow stress of TiAl alloys at interpolated and extrapolated strains with a coefficient of determination () greater than 0.98, while the value of the SCS model was smaller than 0.5 at extrapolated strains. However, both ANN and SCS models performed poorly under new deformation conditions. A hybrid model based on the SCS model and ANN predictions was shown to have a wider generalization capability. The present work provides a comprehensive study on how to choose a predictive model for the flow stress of TiAl alloys under different conditions.
研究了Ti46Al2Cr2Nb合金在应变速率为0.001 - 0.1 s以及温度为910 - 1060 °C时的热变形行为。在给定的变形条件下，TiAl合金的激活能估计为319 kJ/mol。实验结果由不同的预测模型进行预测，包括三种本构模型和三种数据驱动模型。最准确的数据驱动模型和本构模型分别是人工神经网络（ANN）和阿累尼乌斯型应变补偿塞拉斯（SCS）模型。此外，在不同变形条件下检验了ANN模型和SCS模型的泛化能力。在已知变形条件下，ANN模型能够准确预测TiAl合金在插值和外推应变下的流变应力，决定系数（）大于0.98，而在应变外推时SCS模型的 值小于0.5。然而，在新的变形条件下，ANN和SCS模型的表现都很差。基于SCS模型和ANN预测的混合模型具有更广泛的泛化能力。目前的工作对如何在不同条件下为TiAl合金的流变应力选择预测模型进行了全面研究。
