Artificial Neural Network Modeling of Microstructure During C-Mn and HSLA Plate Rolling

An artificial neural network (ANN) model for predicting transformed microstructure in conventional rolling process and thermomechanical controlled process (TMCP) is proposed. The model uses austenite grain size and retained strain, which can be calculated by using microstructure evolution models, to...

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Bibliographic Details
Published in:Journal of iron and steel research, international international, 2009-03, Vol.16 (2), p.80-83
Main Authors: TAN, Wen, LIU, Zhen-yu, WU, Di, WANG, Guo-dong
Format: Article
Language:English
Subjects:
Applied and Technical Physics
artificial neural network
Engineering
ferrite grain size
Machines
Manufacturing
Materials Engineering
Materials Science
Metallic Materials
microstructure
Physical Chemistry
Processes
TMCP
人工神经网络
低合金
奥氏体晶粒尺寸
建模过程
微观结构
模型转化
钢板轧制
高强度
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Summary:An artificial neural network (ANN) model for predicting transformed microstructure in conventional rolling process and thermomechanical controlled process (TMCP) is proposed. The model uses austenite grain size and retained strain, which can be calculated by using microstructure evolution models, together with a measured cooling rate and chemical compositions as inputs and the ferrite grain size and ferrite fraction as outputs. The predicted results show that the model can predict the transformed microstructure which is in good agreement with the measured one, and it is better than the empirical equations. Also, the effect of the alloying elements on transformed products has been analyzed by using the model. The tendency is the same as that in the reported articles. The model can be used further for the optimization of processing parameters, microstructure and properties in TMCP.
ISSN:1006-706X
2210-3988
DOI:10.1016/S1006-706X(09)60032-7