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  4. Modiifed Arrhenius-type Constitutive Model and Artiifcial Neural Network-based Model for Constitutive Relationship of 316LN Stainless Steel during Hot Deformation

钢铁研究学报(英文版), 2015, (8): 721-729.

An HE 1, , Xi-tao WANG 2, , Gan-lin XIE 3, , Xiao-ya YANG 4, , Hai-long ZHANG 5,

1.State Key Laboratory for Advanced Metals and Materials, University of Science and Technology Beijing, Beijing 100083, China

2.Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China

3.State Key Laboratory for Advanced Metals and Materials, University of Science and Technology Beijing, Beijing 100083, China

4.State Key Laboratory for Advanced Metals and Materials, University of Science and Technology Beijing, Beijing 100083, China

5.State Key Laboratory for Advanced Metals and Materials, University of Science and Technology Beijing, Beijing 100083, China

基金项目:   Item Sponsored by National High-tech Research and Development Program (‘863” Program) of China (2012AA03A507,2012AA050901)  

Modiifed Arrhenius-type Constitutive Model and Artiifcial Neural Network-based Model for Constitutive Relationship of 316LN Stainless Steel during Hot Deformation

Keywords： constitutive relation

Hot compression experiments of 316LN stainless steel were carried out on Gleeble-3500 thermo-simulator in deforma-tion temperature range of 1 223?1 423 K and strain rate range of 0.001?1 s-1. The lfow behavior was investigated to evaluate the workability and optimize the hot forging process of 316LN stainless steel pipes. Constitutive relationship of 316LN stainless steel was comparatively studied by a modiifed Arrhenius-type analytical constitutive model considering the effect of strain and by an ar-tiifcial neural network model. The accuracy and effectiveness of two models were respectively quantiifed by the correlation coefif-cient and absolute average relative error. The results show that both models have high reliabilities and could meet the requirements of engineering calculation. Compared with the analytical constitutive model, the artiifcial neural network model has a relatively higher predictability and is easier to work in cooperation with ifnite element analysis software.

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