Prediction of metadynamic softening in a multi-pass hot deformed low alloy steel using artificial neural network

被引：39

作者：

Lin, Y. C. ^{[1
]}

Fang, Xiaoling ^{[2
]}

Wang, Y. P. ^{[3
]}

机构：

[1] Cent S Univ, Sch Mech & Elect Engn, Minist Educ, Key Lab Modern Complex Equipment Design & Extreme, Changsha 410083, Peoples R China

[2] Karamay Vocat & Tech Coll, Dept Chem & Petr Engn, Xinjiang 833600, Peoples R China

[3] Inner Mongolia Polytech Univ, Sch Chem Engn, Hohhot 010062, Peoples R China

来源：

JOURNAL OF MATERIALS SCIENCE | 2008年 / 43卷 / 16期

基金：

中国博士后科学基金;

关键词：

D O I：

10.1007/s10853-008-2832-6

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The metadynamic softening 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 metadynamic softening fractions. The effects of deformation parameters on metadynamic softening behaviors in the hot deformed 42CrMo steel have been investigated by the experimental and predicted results from the developed ANN model. Results show that the effects of deformation parameters, such as strain rate and deformation temperature, on the softening fractions of metadynamic recrystallization are significant. However, the strain (beyond the peak strain) has little influence. A very good correlation between experimental and predicted results indicates that the excellent capability of the developed ANN model to predict the flow stress level and metadynamic softening, the metadynamic recrystallization behaviors were well evidenced.

引用

页码：5508 / 5515

页数：8

共 27 条

[11] Numerical simulation for stress/strain distribution and microstructural evolution in 42CrMo steel during hot upsetting process [J].

Lin, Y. C. ;

Chen, Ming-Song ;

Zhong, Jue .

COMPUTATIONAL MATERIALS SCIENCE, 2008, 43 (04) :1117-1122

[12] Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel [J].

Lin, Y. C. ;

Zhang, Jun ;

Zhong, Jue .

COMPUTATIONAL MATERIALS SCIENCE, 2008, 43 (04) :752-758

[13] Constitutive modeling for elevated temperature flow behavior of 42CrMo steel [J].

Lin, Y. C. ;

Chen, Ming-Song ;

Zhong, Jue .

COMPUTATIONAL MATERIALS SCIENCE, 2008, 42 (03) :470-477

[14] Prediction of 42CrMo steel flow stress at high temperature and strain rate [J].

Lin, Y. C. ;

Chen, Ming-Song ;

Zhong, Jue .

MECHANICS RESEARCH COMMUNICATIONS, 2008, 35 (03) :142-150

[15] Study of static recrystallization kinetics in a low alloy steel [J].

Lin, Y. C. ;

Chen, Ming-Song ;

Zhong, Jue .

COMPUTATIONAL MATERIALS SCIENCE, 2008, 44 (02) :316-321

[16]

LIN YC, 2008, MATER LETT, V62, P2136, DOI DOI 10.1016/J.MATLET.2007.11.032

[17] Kinetics, mechanism and modelling of microstructural evolution during thermomechanical processing of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel [J].

Mandal, Sumantra ;

Sivaprasad, P. V. ;

Dube, R. K. .

JOURNAL OF MATERIALS SCIENCE, 2007, 42 (08) :2724-2734

[18] Plasma surface treatment of AISI 4140 steel for improved corrosion resistance [J].

Mittelstadt, FG ;

Franco, CV ;

Muzart, J ;

DeSouza, AR ;

Cardoso, LP .

JOURNAL OF MATERIALS SCIENCE, 1996, 31 (02) :431-435

[19] Analysis of strengthening mechanisms in a severely-plastically-deformed Al-Mg-Si alloy with submicron grain size [J].

Morris, D. G. ;

Gutierrez-Urrutia, I. ;

Munoz-Morris, M. A. .

JOURNAL OF MATERIALS SCIENCE, 2007, 42 (05) :1439-1443

[20] Prediction of ductile cast iron quality by artificial neural networks [J].

Perzyk, M ;

Kochanski, AW .

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2001, 109 (03) :305-307

← 1 2 3 →