鎳基變形高溫合金動態(tài)軟化行為與組織演變規(guī)律研究

鎳基變形高溫合金動態(tài)軟化行為與組織演變規(guī)律研究DYNAMIC SOFTENING BEHAVIOR AND MICROSTRUCTURE EVOLUTION OF NICKEL-BASE SUPERALLOY

采用Gleeble3500D熱模擬試驗機研究了GH4720Li合金的高溫?zé)嶙冃涡袨椋治隽瞬煌瑹釅嚎s工藝條件下流變力學(xué)曲線特征，建立了表征材料流變力學(xué)特征的包含應(yīng)變參量的雙曲正弦型Arrhenius本構(gòu)關(guān)系模型以及BP人工神經(jīng)網(wǎng)絡(luò)模型，并通過對材料熱變形組織的表征，揭示了GH4720Li合金高溫變形過程中的動態(tài)再結(jié)晶形核機制。結(jié)果表明，包含應(yīng)變參量的雙曲正弦型Arrhenius本構(gòu)關(guān)系模型預(yù)測精度較差，而BP人工神經(jīng)網(wǎng)絡(luò)模型能很好地表征GH4720Li合金熱變形過程中的流變力學(xué)行為，模型預(yù)測值與實驗值的平均相對誤差僅為0.814%。組織分析結(jié)果表明，GH4720Li合金在1140℃條件下動態(tài)再結(jié)晶的主要形核機制為非連續(xù)動態(tài)再結(jié)晶，變形晶粒的晶界為再結(jié)晶晶粒提供形核位置。

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 was 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 are conducted at the deformation temperature range of 1060-1140℃ and strain rate range of 0.001-1s-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 0.1s-1. The peak stress increased 23MPa when the deformation temperature decreased from 1100℃ to 1080℃, only increased 7MPa 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.

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