Modeling Constitutive Relationship of Cu-0.4 Mg Alloy During Hot Deformation

被引:19
|
作者
Ji, Guoliang [1 ]
Yang, Guang [2 ]
Li, Lei [1 ]
Li, Qiang [1 ]
机构
[1] Henan Polytech Univ, Sch Mat Sci & Engn, Jiaozuo 454000, Peoples R China
[2] Henan Polytech Univ, Wanfang Coll Sci & Technol, Jiaozuo 454000, Peoples R China
关键词
artificial neural network; constitutive model; Cu-0.4 Mg alloy; deformation behavior; ARTIFICIAL NEURAL-NETWORK; FLOW BEHAVIOR; AERMET100; STEEL; PREDICT;
D O I
10.1007/s11665-014-0912-0
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
For predicting the high-temperature deformation behavior in a Cu-0.4 Mg alloy, the true stress-strain data from isothermal hot compression tests on a Gleeble-1500 thermo-mechanical simulator, in a wide range of temperatures (500, 600, 700, 750, and 800 A degrees C) and strain rates (0.005, 0.01, 0.1, 1, 5, and 10 s(-1)), were employed to develop the Arrhenius-type constitutive model and the artificial neural network (ANN) constitutive model. Furthermore, prediction ability of the two models for high-temperature deformation behavior was evaluated. Correlation coefficients (R) between the experimental and predicted flow stress for the Arrhenius-type constitutive model and the ANN constitutive model are 0.9860 and 0.9998, respectively, and average absolute relative errors between the experimental and predicted flow stress for these two models are 5.3967% and 0.7401%, respectively. Results show that the ANN constitutive model can accurately predict the high-temperature deformation behavior over a wider range of temperatures and strain rates, while for the Arrhenius-type constitutive model there is greater divergence in the regime of high strain rates and low temperatures.
引用
收藏
页码:1770 / 1779
页数:10
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