Please wait a minute...
金属学报  2018, Vol. 54 Issue (1): 83-92    DOI: 10.11900/0412.1961.2017.00241
  本期目录 | 过刊浏览 |
镍基变形高温合金动态软化行为与组织演变规律研究
王涛1(), 万志鹏1,2, 孙宇2, 李钊1, 张勇1, 胡连喜2
1中国航发北京航空材料研究院先进高温结构材料重点实验室 北京 100095
2哈尔滨工业大学金属精密热加工国家级重点实验室 哈尔滨 150001
Dynamic Softening Behavior and Microstructure Evolution of Nickel Base Superalloy
Tao WANG1(), Zhipeng WAN1,2, Yu SUN2, Zhao LI1, Yong ZHANG1, Lianxi HU2
1 Science and Technology on Advanced High Temperature Structural Materials Laboratory, AEEC Beijing Institute of Aeronautical Materials, Beijing 100095, China
2 National Key Laboratory for Precision Hot Processing of Metals, Harbin Institute of Technology, Harbin 150001, China
引用本文:

王涛, 万志鹏, 孙宇, 李钊, 张勇, 胡连喜. 镍基变形高温合金动态软化行为与组织演变规律研究[J]. 金属学报, 2018, 54(1): 83-92.
Tao WANG, Zhipeng WAN, Yu SUN, Zhao LI, Yong ZHANG, Lianxi HU. Dynamic Softening Behavior and Microstructure Evolution of Nickel Base Superalloy[J]. Acta Metall Sin, 2018, 54(1): 83-92.

全文: PDF(945 KB)   HTML
  
摘要: 

采用Gleeble3500D热模拟试验机研究了GH4720Li合金的高温热变形行为,分析了不同热压缩工艺条件下流变力学曲线特征,建立了表征材料流变力学特征的包含应变参量的双曲正弦型Arrhenius本构关系模型以及BP人工神经网络模型,并通过对材料热变形组织的表征,揭示了GH4720Li合金高温变形过程中的动态再结晶形核机制。结果表明,包含应变参量的双曲正弦型Arrhenius本构关系模型预测精度较差,而BP人工神经网络模型能很好地表征GH4720Li合金热变形过程中的流变力学行为,模型预测值与实验值的平均相对误差仅为0.814%。组织分析结果表明,GH4720Li合金在1140 ℃条件下动态再结晶的主要形核机制为非连续动态再结晶,变形晶粒的晶界为再结晶晶粒提供形核位置。

关键词 GH4720Li合金流变应力行为BP神经元网络非连续动态再结晶    
Abstract

Ni-based supperalloys are widely applied in manufacturing of compressor and turbine discs and polycrystal turbine blades in the hot section of aero-engines, since they possessed excellent mechanical strength and creep resistance at high temperatures. Generally, hot working is an effective way for shaping metals and alloys. Lots of typical metallurgical behaviors occurred, which were related to the hot working parameters, including deformation temperature, strain rate and strain. And BP-ANN (artificial neural network based on the error-back propagation) as well as Arrhenius types models were the two of most acknowledged constitutive models to determine the relationship between the flow behavior and hot deformation parameters of various metals and alloys, at present. In order to investigate the relationship between deformation parameters and flow stress behavior, and precisely simulate the flow behavior during hot deformation processes of GH4720Li alloy, the hot compressive tests of GH4720Li alloy were conducted at the deformation temperature range of 1060~1140 ℃ and strain rate range of 0.001~1 s-1 on Gleeble 3500D thermal simulation testing machine in this work. The relationship between microstructure and hot deformation conditions was identified. The influence of hot processing parameters on flow stress behavior was analyzed. The temperature sensitivity of the flow stress decreased with increasing temperature at a strain rate of 0.1 s-1. The peak stress increased 23 MPa when the deformation temperature decreased from 1100 ℃ to 1080 ℃, only increased 7 MPa when decreased from 1140 ℃ to 1120 ℃. In addition, the Arrhenius model as well as BP artificial neural network model was established according to the true stress-strain curves. It shows that the established BP artificial neural network model can well exhibit the flow stress behavior of GH4720Li alloy compared with the Arrhenius model during hot deformation. The correlation coefficient between experimental findings and predicted flow stress determined by ANN model and Arrhenius model is 0.998 and 0.949, respectively. In addition, the dynamic recrystallization mechanism of the studied alloy was identified according to the deformed microstructure. Microstructure observation of the samples deformed at 1140 ℃ indicated that the discontinuous dynamic recrystallization was the main nucleation mechanism and newly grain nuclei distributed along the deformed grain boundaries. The dynamic recrystallization grain size of GH4720Li alloy decreases with the increase of strain rate when the samples deformed at 1140 ℃ and a strain of 0.8.

Key wordsGH4720Li alloy    flow stress behavior    BP neural network    discontinuous dynamic recrystallization
收稿日期: 2017-06-19     
ZTFLH:  TG146.1  
作者简介:

作者简介 王 涛,男,1982年生,高级工程师,博士

图1  锻造态GH4720Li合金组织的OM像
图2  GH4720Li合金在不同工艺参数条件下的应力-应变曲线
图3  GH4720Li合金ln-lnσ、ln-σ、ln-ln[sinh(ασ)]和ln[sinh(ασ)]-1000/T关系曲线
图4  GH4720Li合金lnZ与ln[sinh(ασ)]关系曲线
图5  GH4720Li合金材料参数随应变以及相关系数随拟合阶数变化曲线
图6  单隐含层神经元网络模型示意图
图7  隐含层节点数对人工神经元网络模型预测能力的影响
图8  GH4720Li合金不同热变形参数条件下流动应力实验值与理论值对比
图9  流动应力实验值与神经元网络模型以及Arrhenius模型残差分析
图10  变形温度1140 ℃、变形速率0.001 s-1、应变0.35和变形温度1140 ℃、变形速率1 s-1、应变0.8条件下GH4720Li合金显微组织的OM像
图11  变形温度1140 ℃、应变0.8时不同应变速率条件下GH4720Li合金显微组织的OM像
[1] Liu W C, Xiao F R, Yao M, et al.Relationship between the lattice constant of γ phase and the content of δ phase, γ'' and γ' phases in inconel 718[J]. Scr. Mater., 1997, 37: 59
[2] Nalawade S A, Sundararaman M, Singh J B, et al.Precipitation of γ' phase in δ-precipitated Alloy 718 during deformation at elevated temperatures[J]. Mater. Sci. Eng., 2010, A527: 2906
[3] Zhang H J, Li C, Liu Y C, et al.Precipitation behavior during high-temperature isothermal compressive deformation of Inconel 718 alloy[J]. Mater. Sci. Eng., 2016, A677: 515
[4] Monajati H, Taheri A K, Jahazi M, et al.Deformation characteristics of isothermally forged UDIMET 720 nickel-base superalloy[J]. Metall. Mater. Trans., 2005, 36A: 895
[5] Liu F F, Chen J Y, Dong J X, et al.The hot deformation behaviors of coarse, fine and mixed grain for Udimet 720Li superalloy[J]. Mater. Sci. Eng., 2016, A651: 102
[6] Jackson M P, Reed R C.Heat treatment of UDIMET 720Li: The effect of microstructure on properties[J]. Mater. Sci. Eng., 1999, A259: 85
[7] Chen J Y, Dong J X, Zhang M C, et al.Deformation mechanisms in a fine-grained Udimet 720LI nickel-base superalloy with high volume fractions of γ' phases[J]. Mater. Sci. Eng., 2016, A673: 122
[8] Chang L T, Jin H, Sun W R.Solidification behavior of Ni-base superalloy Udimet 720Li[J]. J. Alloys Compd., 2015, 653: 266
[9] Sun Y, Wan Z P, Hu L X, et al.Characterization of hot processing parameters of powder metallurgy TiAl-based alloy based on the activation energy map and processing map[J]. Mater. Des., 2015, 86: 922
[10] Wan Z P, Sun Y, Hu L X, et al.Experimental study and numerical simulation of dynamic recrystallization behavior of TiAl-based alloy[J]. Mater. Des., 2017, 122: 11
[11] Zhao J W, Ding H, Zhao W J, et al.Modelling of the hot deformation behaviour of a titanium alloy using constitutive equations and artificial neural network[J]. Comp. Mater. Sci., 2014, 92: 47
[12] Han Y, Qiao G J, Sun J P, et al.A comparative study on constitutive relationship of as-cast 904L austenitic stainless steel during hot deformation based on Arrhenius-type and artificial neural network models[J]. Comp. Mater. Sci., 2013, 67: 93
[13] Peng W W, Zeng W D, Wang Q J, et al.Comparative study on constitutive relationship of as-cast Ti60 titanium alloy during hot deformation based on Arrhenius-type and artificial neural network models[J]. Mater. Des., 2013, 51: 95
[14] Pu E X, Feng H, Liu M, et al.Constitutive modeling for flow behaviors of superaustenitic stainless steel S32654 during hot deformation[J]. J. Iron Steel Res. Int., 2016, 23: 178
[15] Li B, Pan Q L, Yin Z M.Microstructural evolution and constitutive relationship of Al-Zn-Mg alloy containing small amount of Sc and Zr during hot deformation based on Arrhenius-type and artificial neural network models[J]. J. Alloys Compd., 2014, 584: 406
[16] Na Y S, Park N K, Reed R C.Sigma morphology and precipitation mechanism in udimet 720Li[J]. Scr. Mater., 2000, 43: 585
[17] Furrer D U, Fecht H J.γ' formation in superalloy U720LI[J]. Scr. Mater., 1999, 40: 1215
[18] Pang H T, Reed P A S. Effects of microstructure on room temperature fatigue crack initiation and short crack propagation in Udimet 720Li Ni-base superalloy[J]. Int. J. Fatigue, 2008, 30: 2009
[19] Pang H T, Reed P A S . Microstructure effects on high temperature fatigue crack initiation and short crack growth in turbine disc nickel-base superalloy Udimet 720Li[J]. Mater. Sci. Eng., 2007, A448: 67
[20] Monajati H, Jahazi M, Bahrami R, et al.The influence of heat treatment conditions on γ' characteristics in Udimet? 720[J]. Mater. Sci. Eng., 2004, A373: 286
[21] Radis R, Schaffer M, Albu M, et al.Multimodal size distributions of γ' precipitates during continuous cooling of UDIMET 720 Li[J]. Acta Mater., 2009, 57: 5739
[22] Yuan X Y, Chen L Q.Hot deformation at elevated temperature and recrystallization behavior of a high manganese austenitic TWIP steel[J]. Acta Metall. Sin., 2015, 51: 651(袁晓云, 陈礼清. 一种高锰奥氏体TWIP钢的高温热变形与再结晶行为[J]. 金属学报, 2015, 51: 651)
[23] Chen D D, Lin Y C, Zhou Y, et al.Dislocation substructures evolution and an adaptive-network-based fuzzy inference system model for constitutive behavior of a Ni-based superalloy during hot deformation[J]. J. Alloys Compd., 2017, 708: 938
[24] Xu Y, Hu L X, Sun Y.Deformation behaviour and dynamic recrystallization of AZ61 magnesium alloy[J]. J. Alloys Compd., 2013, 580: 262
[25] Cao Y, Di H S, Zhang J C, et al.Research on dynamic recrystallization behavior of Incoloy 800H[J]. Acta Metall. Sin., 2012, 48: 1175(曹宇, 邸洪双, 张洁岑等. 800H合金动态再结晶行为研究[J]. 金属学报, 2012, 48: 1175)
[26] Yin X Q, Park C H, Li Y F, et al.Mechanism of continuous dynamic recrystallization in a 50Ti-47Ni-3Fe shape memory alloy during hot compressive deformation[J]. J. Alloys Compd., 2017, 693: 426
[27] Yu J M, Zhang Z M, Wang Q, et al.Dynamic recrystallization behavior of magnesium alloys with LPSO during hot deformation[J]. J. Alloys Compd., 2017, 704: 382
[28] Sun Y, Zeng W D, Han Y F, et al.Modeling the correlation between microstructure and the properties of the Ti-6Al-4V alloy based on an artificial neural network[J]. Mater. Sci. Eng., 2011, A528: 8757
[29] Ashtiani H R R, Shahsavari P. A comparative study on the phenomenological and artificial neural network models to predict hot deformation behavior of AlCuMgPb alloy[J]. J. Alloys Compd., 2016, 687: 263
[30] Sun Y, Zeng W D, Han Y F, et al.Determination of the influence of processing parameters on the mechanical properties of the Ti-6Al-4V alloy using an artificial neural network[J]. Comp. Mater. Sci., 2012, 60: 239
[31] Sun Y, Zeng W D, Wang S L, et al.Modeling the constitutive relationship of Ti-22Al-25Nb alloy using artificial neural network[J]. J. Plast. Eng., 2009, 16(3): 126(孙宇, 曾卫东, 王邵丽等. 应用人工神经网络建立Ti-22Al-25Nb合金高温本构关系模型[J]. 塑性工程学报, 2009, 16(3): 126)
[32] He A, Wang X T, Xie G L, et al.Modified arrhenius-type constitutive model and artificial neural network-based model for constitutive relationship of 316LN stainless steel during hot deformation[J]. J. Iron Steel Res. Int., 2015, 22: 721
[33] Peng W W, Zeng W D, Wang Q J, et al.Comparative study on constitutive relationship of as-cast Ti60 titanium alloy during hot deformation based on Arrhenius-type and artificial neural network models[J]. Mater. Des., 2013, 51: 95
[34] He G A, Liu F, Huang L, et al.Microstructure evolutions and nucleation mechanisms of dynamic recrystallization of a powder metallurgy Ni-based superalloy during hot compression[J]. Mater. Sci. Eng., 2016, A677: 496
[35] Wan Z P, Sun Y, Hu L X, et al.Dynamic softening behavior and microstructural characterization of TiAl-based alloy during hot deformation[J]. Mater. Charact., 2017, 130: 25
[1] 刘超, 姚志浩, 江河, 董建新. GH4720Li合金毫米级粗大晶粒热变形获得均匀等轴晶粒的可行性及工艺控制[J]. 金属学报, 2021, 57(10): 1309-1319.
[2] 王涛,万志鹏,李钊,李佩桓,李鑫旭,韦康,张勇. 热处理工艺对GH4720Li合金细晶铸锭组织与热加工性能的影响[J]. 金属学报, 2020, 56(2): 182-192.
[3] 万志鹏, 王涛, 孙宇, 胡连喜, 李钊, 李佩桓, 张勇. GH4720Li合金热变形过程动态软化机制[J]. 金属学报, 2019, 55(2): 213-222.