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Acta Metall Sin  2018, Vol. 54 Issue (1): 83-92    DOI: 10.11900/0412.1961.2017.00241
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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
Cite this article: 

Tao WANG, Zhipeng WAN, Yu SUN, Zhao LI, Yong ZHANG, Lianxi HU. Dynamic Softening Behavior and Microstructure Evolution of Nickel Base Superalloy. Acta Metall Sin, 2018, 54(1): 83-92.

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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 words:  GH4720Li alloy      flow stress behavior      BP neural network      discontinuous dynamic recrystallization     
Received:  19 June 2017     
ZTFLH:  TG146.1  

URL: 

https://www.ams.org.cn/EN/10.11900/0412.1961.2017.00241     OR     https://www.ams.org.cn/EN/Y2018/V54/I1/83

Fig.1  OM image of as-forged GH4720Li alloy
Fig.2  Stress-strain (σ-ε) curves of GH4720Li alloy at various hot processing parameters of 0.1 s-1 (a) and 1060 ℃ (b)
  Fig.3 ln-lnσ (a), ln-σ (b), ln-ln[sinh(ασ)] (c) and ln[sinh(ασ)]-1000/T (d) curves of GH4720Li alloy (—strain rate, σ—stress, α—parameter independent of the temperature, which depend on strain, T—absolute temperature)
  Fig.4 lnZ-ln[sinh(ασ)] curve of GH4720Li alloy (B—correlation coefficient)
Fig.5  Relationships between material constant α (a), Q (b), n (c), lnA (d) and strain, as well as correlation coefficient and polynomial order of GH4720Li alloy (Q—activation energy, n and lnA—parameters independent of the temperature, which depend on strain)
Fig.6  Schematic of the artificial neural network(ANN) architecture with one hidden layer
Fig.7  Influence of hidden nodes on the performance of ANN
Fig.8  Comparisons between the experimental and predicted flow curves by ANN model and Arrhenius model of GH4720Li alloy deformed at 1100 ℃ (a) and 1 s-1 (b)
Fig.9  Comparisons of residual error between the experimental and predicted flow stress by ANN model and Arrhenius model
Fig.10  OM images of GH4720Li alloy deformed at T=1140 ℃, =0.001 s-1, ε =0.35 (a) and T=1140 ℃, =1 s-1, ε =0.8 (b) (DRX—dynamic recrystallization)
Fig.11  OM images of GH4720Li alloy deformed at T=1140 ℃, ε =0.8 and =0.001 s-1 (a), =0.01 s-1 (b), =0.1 s-1 (c) and =1 s-1 (d)
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