Modified Arrhenius-type Constitutive Model and Artificial Neural Network-based Model for Constitutive Relationship of 316LN Stainless Steel during Hot Deformation1
An HE1,Xi-tao WANG2,Gan-lin XIE1,Xiao-ya YANG1,Hai-long ZHANG1
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
Modified Arrhenius-type Constitutive Model and Artificial Neural Network-based Model for Constitutive Relationship of 316LN Stainless Steel during Hot Deformation1
An HE1,Xi-tao WANG2,Gan-lin XIE1,Xiao-ya YANG1,Hai-long ZHANG1
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
ժҪ Hot compression experiments of 316LN stainless steel were carried out on Gleeble-3?500 thermo-simulator in deforma-tion temperature range of 1?223?1?423 K and strain rate range of 0.001?1 s-1. The flow 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 modified Arrhenius-type analytical constitutive model considering the effect of strain and by an ar-tificial neural network model. The accuracy and effectiveness of two models were respectively quantified by the correlation coeffi-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 artificial neural network model has a relatively higher predictability and is easier to work in cooperation with finite element analysis software.
Abstract��Hot compression experiments of 316LN stainless steel were carried out on Gleeble-3?500 thermo-simulator in deforma-tion temperature range of 1?223?1?423 K and strain rate range of 0.001?1 s-1. The flow 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 modified Arrhenius-type analytical constitutive model considering the effect of strain and by an ar-tificial neural network model. The accuracy and effectiveness of two models were respectively quantified by the correlation coeffi-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 artificial neural network model has a relatively higher predictability and is easier to work in cooperation with finite element analysis software.
��������:National High-Tech Research and Development Program (����863���� Program) of China;National High-Tech Research and Development Program (����863���� Program) of China
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An HE,Xi-tao WANG,Gan-lin XIE,Xiao-ya YANG,Hai-long ZHANG. Modified Arrhenius-type Constitutive Model and Artificial Neural Network-based Model for Constitutive Relationship of 316LN Stainless Steel during Hot Deformation1[J]. �й������ڿ���, 2015, 22(8): 721-729.
An HE,Xi-tao WANG,Gan-lin XIE,Xiao-ya YANG,Hai-long ZHANG. Modified Arrhenius-type Constitutive Model and Artificial Neural Network-based Model for Constitutive Relationship of 316LN Stainless Steel during Hot Deformation1. Chinese Journal of Iron and Steel, 2015, 22(8): 721-729.
2015, Vol. 22 
