| COMPUTATIONAL SIMULATION |
|
|
|
|
|
Flow Stress Prediction Model and Processing Map of Mg-Sm-Zn-Zr Alloy
Based on GA-BP Neural Network |
| CHANG Ruohan1, CAI Zhongyi1, CHENG Liren2, CHE Chaojie1, CHI Jiaxuan1
|
1 Roll Forging Research Institute, Jilin University, Changchun 130025;
2 State Key Laboratory of Rare Earth Resources
Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022 |
|
|
|
|
Abstract The flow stress behavior of Mg-Sm-Zn-Zr alloy was studied by isothermal compression experiment on Gleeble-1500D thermal-mechanical test machine at deformation temperatures of 350-450 ℃ and strain rates of 0.001-1 s-1. The genetic algorithm BP neural network (GA-BP) was developed to predict the flow stress, and the comparative study on GA-BP model and strain compensated Arrhenius-type constitutive model was presented. Based on the prediction stress, the processing map was established under instability criteria of Murthy, finally the rationality of the designed processing map was verified by microstructure. The results showed that the correlation coefficient was 0.999 and the average relative error was 1.469% for the GA-BP model, which indicated that the GA-BP model could be more accurate in predicting the flow stress than constitutive model considering the compensation of strain. The processing map was properly designed, and the map confirmed the temperatures of 400-450 ℃ and strain rates of 0.001-0.03 s-1 as the optimum process parameters. The dynamic recrystallization (DRX) occurred in the deformed samples under the above parameters.
|
|
Published: 25 March 2017
Online: 2018-05-02
|
|
|
|
|
1 Mordike B L, Ebert T. Magnesium: properties-applications-potential [J]. Mater Sci Eng A,2001,302(1):37.
2 Chen Q, Xia X S, Yuan B G, et al. Hot workability behavior of as-cast Mg-Zn-Y-Zr alloy[J]. Mater Sci Eng A,2014,593:38.
3 Yu H, Kim Youngmin, Yu H S, et al. Hot deformation behavior and hot workability of Mg-Zn-Zr-Ce alloy[J]. Acta Metall Sin,2012,48(9):1123(in Chinese).
余晖, Kim Youngmin, 于化顺, 等. Mg-Zn-Zr-Ce合金高温变形行为与热加工性能研究[J]. 金属学报,2012,48(9):1123.
4 陈振华. 镁合金[M]. 北京: 化学工业出版社,2004:2.
5 Rokhlin L L. Magnesium alloys containing rare earth metals: Structure and properties [M]. Bocca Raton:CRC Press,2003.
6 Wu D G, Yan S H, Li Z A, et al. Effect of samarium on corrosion behavior of as-cast AZ92 magnesium alloy[J]. Chin J Rare Metals,2013,37(2):199(in Chinese).
吴道高, 颜世宏, 李宗安, 等. 稀土Sm对 AZ92镁合金耐腐蚀性能的影响[J].稀有金属,2013,37(2):199.
7 Yang M, Pan F. Comparative studies on as-cat microstructures and mechanical properties between Mg-3Ce-1.2Mn-0.9Sc and Mg-3Ce-1.2Mn-1Zn magnesium alloys [J]. Trans Nonferrous Metals Soc China,2012,22(1):53.
8 Zheng J, Wang Q, Jin Z, et al. Effect of Sm on the microstructure, mechanical properties and creep behavior of Mg-0.5Zn-0.4Zr based alloys [J]. Mater Sci Eng A,2010,527(7):1677.
9 Xia X, Luo A A, Stone D S. Precipitation sequence and kinetics in a Mg-4Sm-1Zn-0.4 Zr (wt%) alloy[J]. J Alloys Compd,2015,649:649.
10 Li H Y, Wang X F, Wei D D, et al. A comparative study on modified Zerilli-Armstrong, Arrhenius-type and artificial neural network models to predict high-temperature deformation behavior in T24 steel[J]. Mater Sci Eng A,2012,536:216.
11 Yue Y W, Wen T, Liu L T, et al. Predicted processing map of TC4 titanium alloy based on BP neural network[J]. Chin J Rare Metals,2014,38(4):567(in Chinese).
岳远旺, 温彤, 刘澜涛, 等. 基于BP神经网络预测的TC4热加工图[J]. 稀有金属,2014,38(4):567.
12 Haghdadi N, Zarei-Hanzaki A, Khalesian A R, et al. Artificial neural network modeling to predict the hot deformation behavior of an A356 aluminum alloy[J]. Mater Des,2013,49:386.
13 Robi P S, Dixit U S. Application of neural networks in generating processing map for hot working[J]. J Mater Process Technol,2003,142(1):289.
14 Prasad Y, Gegel H L, Doraivelu S M, et al. Modeling of dynamic material behavior in hot deformation: Forging of Ti-6242[J]. Metall Trans A,1984,15(10):1883.
15 Chen Y F, Jiang H D, Tang M. Constitutive equation of 38MnVTi steel and processing map[J]. Mater Rev:Res,2014,28(11):149(in Chinese).
陈元芳, 江华德, 汤萌. 38MnVTi 钢本构方程及加工图[J]. 材料导报:研究篇,2014,28(11):149.
16 Chen Q, Wang Y. Hot working behavior of delta-processed GH4169 alloy[J]. Chin J Nonferrous Metals,2015,25(9):2727(in Chinese).
陈前, 王岩. δ相时效态GH4169合金的热加工行为[J]. 中国有色金属学报,2015,25(10):2727.
17 Lin Y C, Li L T, Xia Y C, et al. Hot deformation and processing map of a typical Al-Zn-Mg-Cu alloy[J]. J Alloys Compd,2013,550:438.
18 Narayana Murty S V S, Nageswara Rao B, at al. Instability criteria for hot deformation of materials[J]. Int Mater Rev,2000,45(1):15.
|
|
|
|
渝公网安备50019002502923号 © Editorial Office of Materials Reports.
2017, Vol. 31 
