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| Modified artificial neural network model with an explicit expression to describe flow behavior and processing maps of Ti2AlNb-based superalloy |
| Yan-qi Fu1,2, Qing Zhao1,2, Man-qian Lv1,2, Zhen-shan Cui1,2 |
| 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 |
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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.
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| Cite this article: |
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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.
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| [1] |
Ming-jun Zhong, Ke-lu Wang, Shi-qiang Lu, Xin Li, Xuan Zhou, Rui Feng. Hot deformation behavior and process parameters optimization of Ti– 6Al–7Nb alloy using constitutive modeling and 3D processing map[J]. JOURNAL OF IRON AND STEEL RESEARCH,INTERNATIONAL, 2021, 28(7): 862-873. |
| [2] |
Yang-yang Zhu, Li-kui Ning, Tong-zheng Xin, En-ze Liu, Jian Tong, Zheng Tan, Yang-tao Zhou, Zhi Zheng. Hot deformation behavior and microstructure evolution of an Fe–30Cr–2Mo ultra-pure super ferritic stainless steel[J]. JOURNAL OF IRON AND STEEL RESEARCH,INTERNATIONAL, 2021, 28(10): 1291-1304. |
| [3] |
Hai Wang, Dong Liu, Jian‑guo Wang, Hai‑ping Wang, Yang Hu, Hao‑dong Rao. Characterization of hot deformation behavior of 30Si2MnCrMoVE low-alloying ultra-high-strength steel by constitutive equations and processing maps[J]. JOURNAL OF IRON AND STEEL RESEARCH,INTERNATIONAL, 2020, 27(7): 807-819. |
| [4] |
Chen Dong, Zheng‑dong Liu, Zheng‑zong Chen, Zhen Liu, Jia‑zhuang Zheng, Xi‑tao Wang. Hot deformation behavior of a heat-resistant alloy without γ′-phase[J]. JOURNAL OF IRON AND STEEL RESEARCH,INTERNATIONAL, 2020, 27(7): 820-833. |
| [5] |
Meng-du Peng, Jie Shi, Wen-chao Yu, Bing Cui, Ting Sun, Xiao-yuan Li, Mao-qiu Wang. Hot working process optimization of Fe–20Mn–19Cr–0.5C–0.6N steel by activation energy, power dissipation and microstructural evolution[J]. JOURNAL OF IRON AND STEEL RESEARCH,INTERNATIONAL, 2019, 26(8): 856-865. |
| [6] |
Peng-tao Liu,, ? Qing-xue Huang,, ? Li-feng Ma, ? Tao Wang. Characterization of hot deformation behavior of wear-resistant steel BTW1 using processing maps and constitutive equations[J]. , 2018, 25(10): 1054-1061. |
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2021, Vol. 28 
