Record number :
1183543
Title of article :
Prediction of static recrystallization in a multi-pass hot deformed low-alloy steel using artificial neural network
Author/Authors :
Y.C. Lin، نويسنده , , Ge Liu، نويسنده , , Ming-Song Chen، نويسنده , , Jue Zhong، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2009
Pages :
6
From page :
4611
To page :
4616
Abstract :
The static recrystallization behaviors in 42CrMo steel were investigated by isothermal interrupted hot compression tests. Based on the experimental results, an efficient artificial neural network (ANN) model was developed to predict the flow stress and static recrystallized fractions. The effects of the deformation temperature, strain rate and deformation degree, as well as initial grain sizes, on the static recrystallization behaviors in two-pass hot compressed 42CrMo steel were investigated by the experiments and ANN model. A very good correlation between the experimental and predicted results from the developed ANN model has been obtained, which indicates that the excellent capability of the developed ANN model to predict the flow stress level and static recrystallization behaviors in two-pass hot deformed 42CrMo steel. The effects of strain rate, deformation temperature and degree of deformation on the static recrystallization behaviors are significant, while those of the initial austenite grain size are slight.
Keywords :
Static recrystallization , 42CrMo steel , Artificial neural networks , Hot compression deformation
Journal title :
Journal of Materials Processing Technology
Serial Year :
2009
Link To Document :
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