A comparative study on constitutive relationship of as-cast 904L austenitic stainless steel during hot deformation based on Arrhenius-type and artificial neural network models

被引:137
|
作者
Han, Ying [1 ]
Qiao, Guanjun [1 ]
Sun, JiaPeng [1 ]
Zou, Dening [2 ]
机构
[1] Xi An Jiao Tong Univ, State Key Lab Mech Behav Mat, Xian 710049, Peoples R China
[2] Xian Univ Architecture & Technol, Sch Met & Engn, Xian 710055, Peoples R China
关键词
Austenitic stainless steel; Hot deformation; Constitutive relationship; Artificial neural network; Arrhenius-type; TEMPERATURE FLOW BEHAVIOR; PREDICT; ALLOY; STRESS; EVALUATE; EQUATION;
D O I
10.1016/j.commatsci.2012.07.028
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Constitutive relationship of as-cast 904L austenitic stainless steel is comparatively investigated by the Arrhenius-type constitutive model incorporating the strain effect and back-propagation (BP) neural network. The experimental true stress-true strain data were obtained from hot compression tests on the Gleeble-1500D thermo-mechanical simulator in the temperature range of 1000-1150 degrees C and strain rate range of 0.01-10 s(-1). The corrected data with the friction and the temperature compensations were employed to develop the Arrhenius-type model and BP neural network respectively. The accuracy and reliability of the models were quantified by employing statistical parameters such as the correlation coefficient and absolute average error. The results show that the proposed models have excellent predictabilities of flow stresses for the present steel in the specified deformation conditions. Compared with the Arrhenius-type model, the optimized BP neural network model has more accuracy and capability in describing the compressive deformation behavior at elevated temperature for as-cast 904L austenitic stainless steel. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:93 / 103
页数:11
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