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罗皎,高峻,李淼泉.高温变形过程中Ti-6Al-2Zr-2Sn-2Mo-1.5Cr-2Nb合金的流动应力和晶粒尺寸模型[J].稀有金属材料与工程(英文),2018,47(6):1716~1722.[Luo Jiao,Gao Jun and Li Miaoquan.Modelling of Flow Stress and Grain Size in the High Temperature Deformation of Ti-6Al-2Zr-2Sn-2Mo-1.5Cr-2Nb Alloy[J].Rare Metal Materials and Engineering,2018,47(6):1716~1722.]
Modelling of Flow Stress and Grain Size in the High Temperature Deformation of Ti-6Al-2Zr-2Sn-2Mo-1.5Cr-2Nb Alloy
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Received:June 07, 2016  Revised:October 11, 2016
DOI:
Key words: titanium alloys  fuzzy neural network  microstructure  flow stress  grain size
Foundation item:国家自然科学基金(51575446);陕西省自然科学基金(2016JQ5070)
Author NameAffiliation
Luo Jiao,Gao Jun and Li Miaoquan  
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Abstract:
      After the optical micrography (OM) and scanning electron microscopy (SEM) observations, the grain size of primary α phase was measured via a quantitative metallography image analysis software. The effect of deformation temperature and strain rate on the microstructure was discussed. A Pi-sigma fuzzy neural network (FNN), in which the layers of neural networks were organized into a feed-forward system, was used to predict the flow stress and the grain size during isothermal compression of Ti-6Al-2Zr-2Sn-2Mo-1.5Cr-2Nb alloy. The comparisons of the predicted flow stress and grain size for the sample data or the non-sample data with the experimental results were given to train the models and confirm the validity in present study. The results show that the accuracy of prediction from the Pi-sigma FNN models is much high, and the Pi-sigma FNN approach can efficiently describe the non-linear and complex relationship of titanium alloys.