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刘晓燕,杨成,杨西荣.基于人工神经网络的超细晶纯钛热变形本构模型研究[J].稀有金属材料与工程(英文),2018,47(10):3038~3044.[liuxiaoyan,yangcheng and yangxirong.A Constitutive Model of Ultrafine Grained Pure Titanium at ElevatedTemperature Based on Artificial Neural Network[J].Rare Metal Materials and Engineering,2018,47(10):3038~3044.]
A Constitutive Model of Ultrafine Grained Pure Titanium at ElevatedTemperature Based on Artificial Neural Network
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Received:January 08, 2017  Revised:September 07, 2018
DOI:
Key words: Ultrafine grained pure titanium  artificial neural network  Arrhenius constitutive equations  flow stress
Foundation item:国家自然科学基金(51474170)和陕西省自然科学基金(2016JQ5026)联合资助
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liuxiaoyan,yangcheng and yangxirong  
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Abstract:
      Ultrafine grained (UFG) pure titanium was prepared by ECAP up to four passes. The hot compression tests were conducted in the different temperatures (250~450 ℃) and the strain rates of 10-5~1s-1.The artificial neural network (ANN) and Arrhenius constitutive equation were used for establishing constitutiveSmodel of UFG pure titanium, respectively. The experimental results show that the flow stress increased with the increase of strain at the beginning of the deformation, then increased slowly. Finally, the stress reached a stable value. The experimental value and the predicted value of flow stress showed that the average absolute relative errors obtained from the artificial neural network model and Arrhenius constitutive equations were 2.1% and 11.54%, respectively. The correlation coefficient of the artificial neural network model and Arrhenius constitutive equations were 0.9979 and 0.9464, respectively. It means that the artificial neural network model can more accurately describe the constitutive relations of UFG pure titanium. By comparing the error of the two models under different temperatures, it can be find that artificial neural network model has better stability under the condition of high temperature.