1 School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 2 Institute of Forming Technology and Equipment, Shanghai Jiao Tong University, Shanghai 200030, China
Modified artificial neural network model with an explicit expression to describe flow behavior and processing maps of Ti2AlNb-based superalloy
1 School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China 2 Institute of Forming Technology and Equipment, Shanghai Jiao Tong University, Shanghai 200030, China
摘要 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 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.
Abstract：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 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.
Yan-qi Fu,Qing Zhao,Man-qian Lv, et al. Modified artificial neural network model with an explicit expression to describe flow behavior and processing maps of Ti2AlNb-based superalloy[J]. Journal of Iron and Steel Research International, 2021, 28(11): 1451-1462.
2021, Vol. 28 
