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Modified Arrhenius-type Constitutive Model and Artificial Neural Network-based Model for Constitutive Relationship of 316LN Stainless Steel during Hot Deformation 被引量:8

Modified Arrhenius-type Constitutive Model and Artificial Neural Network-based Model for Constitutive Relationship of 316LN Stainless Steel during Hot Deformation
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摘要 Hot compression experiments of 316 LN stainless steel were carried out on Gleeble-3500 thermo-simulator in deformation 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 316 LN stainless steel pipes. Constitutive relationship of 316 LN stainless steel was comparatively studied by a modified Arrhenius-type analytical constitutive model considering the effect of strain and by an artificial neural network model. The accuracy and effectiveness of two models were respectively quantified by the correlation coefficient 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. 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 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.
出处 《Journal of Iron and Steel Research(International)》 SCIE EI CAS CSCD 2015年第8期721-729,共9页 钢铁研究学报(英文版)
基金 Sponsored by National High-tech Research and Development Program(‘‘863"Program)of China(2012AA03A507,2012AA050901)
关键词 人工神经网络模型 阿伦尼乌斯 不锈钢管 本构模型 本构关系 变形过程 有限元分析软件 平均相对误差 constitutive relation artificial neural network stainless steel hot deformation
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