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

作　　者：TAN Wen LIU Zhen-yu WU Di WANG Guo-dong 

机构地区：[1]The State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110004, Liaoning, China

出　　处：《Journal of Iron and Steel Research International》2009年第2期80-83,共4页

基　　金：Item Sponsored by National Natural Science Foundation of China (50474086);Program for New Century Excellent Talents in University (NECT-04-0278)

摘　　要：An artificial neural network （ANN） model for predicting transformed mierostrueture in conventional rolling process and therrnomechanical 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 re- suits 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, mierostructure and properties in TMCP.An artificial neural network （ANN） model for predicting transformed mierostrueture in conventional rolling process and therrnomechanical 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 re- suits 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, mierostructure and properties in TMCP.

关 键 词：artificial neural network TMCP MICROSTRUCTURE ferrite grain size 

分 类 号：TG335.5[金属学及工艺—金属压力加工] TP183[自动化与计算机技术—控制理论与控制工程]