Modiifed Arrhenius-type Constitutive Model and Artiifcial Neural Network-based Model for Constitutive Relationship of 316LN Stainless Steel during Hot Deformation

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An HE
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1 Xi-tao WANG 已出版文章查询
Xi-tao WANG
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2 Gan-lin XIE 已出版文章查询
Gan-lin XIE
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1 Xiao-ya YANG 已出版文章查询
Xiao-ya YANG
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1 Hai-long ZHANG 已出版文章查询
Hai-long ZHANG
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1State Key Laboratory for Advanced Metals and Materials, University of Science and Technology Beijing, Beijing 100083, China

2Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China


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Hot compression experiments of 316LN stainless steel were carried out on Gleeble-3500 thermo-simulator in deforma-tion temperature range of 1 223?1 423 K and strain rate range of 0.001?1 s-1. The lfow behavior was investigated to evaluate the workability and optimize the hot forging process of 316LN stainless steel pipes. Constitutive relationship of 316LN stainless steel was comparatively studied by a modiifed Arrhenius-type analytical constitutive model considering the effect of strain and by an ar-tiifcial neural network model. The accuracy and effectiveness of two models were respectively quantiifed by the correlation coefif-cient and absolute average relative error. The results show that both models have high reliabilities and could meet the requirements of engineering calculation. Compared with the analytical constitutive model, the artiifcial neural network model has a relatively higher predictability and is easier to work in cooperation with ifnite element analysis software.

[1] P. Changizian;A. Zarei-Hanzaki;Ali A. Roostaei .The high temperature flow behavior modeling of AZ81 magnesium alloy considering strain effects[J].Materials & design,2012(Aug.):384-389.

[2] C.M.Sellars;W.J.McTegart .[J].Acta Metall,1966,14:1136-1138.

[3] Y.C.Lin;M.S.Chen;J.Zhang .[J].Mechanics Research Communications,2008,35:142-150.

[4] D.Samantaray;S.Mandal;A.K.Bhaduri .[J].Comput Mater Sci,2009,47:568-576.

[5] A. Mirzaei;A. Zarei-Hanzaki;N. Haghdadi;A. Marandi.Constitutive description of high temperature flow behavior of Sanicro-28 super-austenitic stainless steel[J].Materials Science & Engineering, A. Structural Materials: Properties, Misrostructure and Processing,2014:76-82.

[6] F.Feng;S.Huang;Z.Meng;J.Hu Y.Lei M.Zhou Z.Yang .[J].MATERIALS SCIENCE & ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING,2014,594:334-343.

[7] Y.C.Lin;Y.C.Xia;X.M.Chen;M.S.Chen .[J].Comput Mater Sci,2010,50:227-233.

[8] Y.C.Lin;M.S.Chen;J.Zhong .[J].Comput Mater Sci,2008,42:470-477.

[9] A. Ma;F. Roters;D. Raabe .A dislocation density based constitutive model for crystal plasticity FEM including geometrically necessary dislocations[J].Acta materialia,2006(8):2169-2179.

[10] S.Ghosh;P.Chakraborty .[J].International Journal of Fatigue,2013,48:231-246.

[11] Z.M.Huang .[J].Computers & Structures,2007,77:270-279.

[12] C.J.Bennett .[J].Comput Mater Sci,2013,70:114-122.

[13] K. Lu .The Future of Metals[J].Science,2010(Apr.16 TN.5976):319-320.

[14] A.He;G.L.Xie;H.L.Zhang;X.Wang .[J].Mater Des,2013,52:677-685.

[15] Y.C. Lin;Xiao-Min Chen .A critical review of experimental results and constitutive descriptions for metals and alloys in hot working[J].Materials & design,2011(4):1733-1759.

[16] R.Liang;A.S.Khan .[J].International Journal of Plasticity,1999,15:963-980.

[17] J.L.Chaboche .[J].International Journal of Plasticity,2008,24:1642-1693.

[18] G.R.Johnson;W.H.Cook.[A].The Hague,The Netherlands,1983:541-543.

[19] P.J.Zerilli;R.W.Armstrong .[J].Journal of Applied Physics,1987,61:1816-1825.

[20] C.Zener;H.Hollomon .[J].Journal of Applied Physics,1944,15:22-32.

[21] J.Jonas;C.M.Sellars;W.J.McTegart .[J].Int Metal Rev,1969,14:1-24.

[22] H.Shi;A.J.McLaren;C.M.Sellars .[J].Materials Science and Technology,1997,13:210-216.

[23] Y.C. Lin;Ming-Song Chen;Jue Zhong .Effects Of Deformation Temperatures On Stress/strain Distribution And Microstructural Evolution Of Deformed 42crmo Steel[J].Materials & design,2009(3):908-913.

[24] A.He;L.Chen;S.Wang;L.Huangfu .[J].Adv Mater Res,2013,683:488-491.

[25] A.He;L.Chen;S.Hu;C.Wang L.Huangfu .[J].Mater Des,2013,46:54-60.

[26] A.He;G.L.Xie;H.L.Zhang;X.Wang .[J].Mater Des,2014,56:122-127.

[27] D.Samantaray;S.Mandal;A.K.Bhaduri .[J].Comput Mater Sci,2009,47:568-576.

[28] D.Samantaray;S.Mandal;A.K.Bhaduri .[J].MATERIALS SCIENCE & ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING,2011,528:5204-5211.

[29] D.Samantaray;S.Mandal;A.K.Bhaduri .[J].Mater Des,2010,31:981-984.

[30] G. L Xie;X. T. Wang;L Chen .Microstructural modelling of dynamic recrystallisation in Nb microalloyed steels[J].Materials Science and Technology: MST: A publication of the Institute of Metals,2012(7):778-782.

[31] Y.Han;G.J.Qiao;J.P.Sun;D.Zou .[J].Comput Mater Sci,2013,67:93-103.

[32] B.Li;Q.L.Pan;Z.M.Yin .[J].Journal of Alloys and Compounds,2014,584:406-416.

[33] X.Xiao;G.Q.Liu;B.F.Hu;X.Zheng L.N.Wang S.J.Chen A.Ullah .[J].Comput Mater Sci,2012,62:227-234.

[34] G.L.Ji;F.G.Li;Q.H.Li;H.Li Z.Li .[J].MATERIALS SCIENCE & ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING,2011,528:4774-4782.

[35] Q.B.Liu;Z.Ji;M.B.Liu;S.C.Wu .[J].Journal of Materials Processing Technology,1996,62:206-210.

[36] R.Haj-Ali;H.K Kim;S.W.Koh;A.Saxena R.Tummala .[J].International Journal of Plasticity,2008,24:371-396.

[37] S.Mandal;P.V.Sivaprasad;S.Venugopal;K.P.N.Murthy B.Raj .[J].MATERIALS SCIENCE & ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING,2008,485:571-580.

[38] Y.Sun;W.D.Zeng;X.Ma .[J].INTERMETALLICS,2011,19:1014-1019.

[39] Y.C.Lin;J.Zhang;J.Zhong .[J].Comput Mater Sci,2008,43:752-758.

[40] H.J.McQueen;S.Yue;N.D.Ryan .[J].Journal of Materials Processing Technology,1995,53:293-310.

[41] H.J.McQueen;N.D.Ryan .[J].MATERIALS SCIENCE & ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING,2002,322:43-63.

[42] C.Phaniraj;D.Samantaray;S.Mandal;A.K.Bhaduri .[J].MATERIALS SCIENCE & ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING,2011,528:6066-6071.

[43] A.Laasraoui;J.J Jonas .[J].Metallurgical and Materials Transactions A:Physical Metallurgy and Materials Science,1991,22:1545-1558.

[44] M.Wei.Study on Dynamic Recrystallization and Processing Map of 316LN Stainless Steel[M].Kunming University of Science and Technol-ogy,Kunming,2012

[45] M.M.Chen.Experimental and Simulation Study of Microstructure Evolution of 316LN Stainless Steel during Forging[M].Taiyuan Univer-sity of Science and Technology,Taiyuan,2010

[46] K.Cai;J.T.Xia;L.T.Li;Z.L.Gui .[J].Comput Mater Sci,2005,34:166-172.

[47] X.Xiao;G.Q.Liu;B.F.Hu .[J].Comput Mater Sci,2012,62:227-234.

[48] X.Ma;W.Zeng;Y.Sun;K.Wang Y.Lai Y.Zhou .[J].MATERIALS SCIENCE & ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING,2012,538:182-189.

[49] E.M. Bezerra;A.C. Ancelotti;L.C. Pardini;J.A.F.F. Rocco;K. Iha;C.H.C. Ribeiro .Artificial neural networks applied to epoxy composites reinforced with carbon and E-glass fibers: Analysis of the shear mechanical properties[J].Materials Science & Engineering, A. Structural Materials: Properties, Misrostructure and Processing,2007(1/2):177-185.

[50] A. R. Shahani;S. Setayeshi;S. A. Nodamaie;M. A. Asadi;S. Rezaie .Prediction of influence parameters on the hot rolling process using finite element method and neural network[J].Journal of Materials Processing Technology,2009(4):1920-1935.

[51] M.Bagheripoor;H.Bisadi .[J].Appl Math Modell,2013,37:4593-4607.

[52] R.Kapoor;D.Pal;J.K.Chakravartty .[J].Journal of Materials Processing Technology,2005,169:199-205.

[53] Y. C. Lin;Ge Liu;Ming-Song Chen;Jue Zhong .Prediction of static recrystallization in a multi-pass hot deformed low-alloy steel using artificial neural network[J].Journal of Materials Processing Technology,2009(9):4611-4616.


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