Present Research Situation and Prospect of Multi-Scale Design in Novel Co-Based Superalloys: A Review

doi:10.11900/0412.1961.2019.00159

Acta Metall Sin

2020, Vol. 56

Issue (1): 1-20 DOI: 10.11900/0412.1961.2019.00159

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Present Research Situation and Prospect of Multi-Scale Design in Novel Co-Based Superalloys: A Review

LIU Xingjun^1,^2,³(

),CHEN Yuechao³,LU Yong³,HAN Jiajia³,XU Weiwei³,GUO Yihui³,YU Jinxin³,WEI Zhenbang³,WANG Cuiping³(

)

1. Institute of Materials Genome and Big Data, Harbin Institute of Technology, Shenzhen 518055, China
2. School of Materials Science and Engineering, Harbin Institute of Technology, Shenzhen 518055, China
3. College of Materials and Fujian Provincial Key Laboratory of Materials Genome, Xiamen University, Xiamen 361005, China

Cite this article:

LIU Xingjun, CHEN Yuechao, LU Yong, HAN Jiajia, XU Weiwei, GUO Yihui, YU Jinxin, WEI Zhenbang, WANG Cuiping. Present Research Situation and Prospect of Multi-Scale Design in Novel Co-Based Superalloys: A Review. Acta Metall Sin, 2020, 56(1): 1-20.

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Abstract

In recent years, the development of material genetic methods, together with multi-scale material design theory and calculation methods has provided new ideas for the alloy design of novel Co-based superalloys. Based on the published results of multi-scale design and the research work of our laboratory, this paper systematically summarizes the present research status of multi-scale design methods in the field of novel Co-based superalloys. A review of multi-scale calculation methods including first-principle calculation, CALPHAD, phase field simulation, and machine learning is presented in this paper. The development trend of multi-scale design in novel Co-based superalloys is prospected.

Key words: superalloy multi-scale design computational materials science materials genome

Received: 20 May 2019

ZTFLH:

TG146.1

Fund: National Key Research and Development Program of China(2017YFB0702901);Key-Areas Research and Development Program of Guangdong Province(2019B0109430);National Natural Science Foundation of China(51831007)

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	LIU Xingjun
	CHEN Yuechao
	LU Yong
	HAN Jiajia
	XU Weiwei
	GUO Yihui
	YU Jinxin
	WEI Zhenbang
	WANG Cuiping

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https://www.ams.org.cn/EN/10.11900/0412.1961.2019.00159 OR https://www.ams.org.cn/EN/Y2020/V56/I1/1

Fig.1 Comparison of diffusivities of transition metal solutes in fcc cobalt calculated using various levels of approximation against experimentally measured values (D—self-diffusion coefficient, T—temperature, v^*—effective frequency)^[24]

Fig.2 Calculated defect energies (ΔE_d) for Co₃Al with an X substituting either Al- or Co-sublattice (a)^[34], formation energies of X-substituted L1₂ Co₃V compound (b)^[36], and the enthalpies of formation of the Co-Ta-X compounds in L1₂ and D0₁₉ structures with the alloying elements occupying Co sites (c)^[35]

Fig.3 Isosurface plots of the deformed charge density difference for the pure (a~c) and Ta-doped (d~f) Co₃(Al, W) phases at [001] tensile strains (ε) of ε=0 (without deformation) (a, d), ε=0.32 (before latice instability) (b, e) and ε=0.46 (after lattice instability) (c, f)^[43]

Table 1 Thermodynamics database of novel Co-based superalloys

Fig.4 Isothermal sections of Co-Al-W ternary phase diagram at 900 ℃ (a)^[3], and calculated phase diagram of Co-Al-W-xNi quaternary system at 900 ℃ with x=10 (b), x=20 (c) and x=30 (d)

Fig.5 Calculated curves of phase fraction vs temperature in Co-10Al-10W (a), Co-10Al-10W-10Ni (b), Co-10Al-10W-20Ni (c) and Co-10Al-10W-30Ni (d) alloys

Fig.6 Calculated phase diagrams of Co-xNi-Al-W-15Cr alloys at 900 ℃ with x=20 (a), x=30 (b) and x=50 (c)

Fig.7 Alloy design and simulation process

Fig.8 Self-diffusion coefficients for Al (a) and Nb (b) elements in the bcc, fcc and hcp structures^[91]

Fig.9 Impurity diffusion coefficients of Ni (a) and W (b) elements in fcc Co

Fig.10 Comparisons between the calculated and measured results of main interdiffusion coef?cients (

D ? i i

) (a) and diffusion paths (b) for the fcc Co-Cr-Mo system at 1473 K^[97]

Fig.11 Microstructure evolution simulation diagrams of γ' precipitation phase in Co-9.0Al-9.0W alloy with ageing time (t) at 900 ℃(a) t=2.75 h (b) t=11 h (c) t =27.5 h (d) t =82.5 h (f) t =165 h (g) t =275 h

Fig.12 Microstructure evolutions of γ' phase in Co-9.0Al-9.0W alloy with time under applied stress at 900 ℃(a) t=2.75 h (b) t=11 h (c) t=27.5 h (d) t=82.5 h (f) t=165 h (g) t=275 h

Fig.13 Microstructure evolutions of γ', γ and D0₁₉ phases near the grain boundary in Co-9.3Al-10.5W alloy at 900 ℃(a) t=250 h, Al (b) t=250 h, W (c) t=500 h, Al (d) t=500 h, W

Fig.14 Framework of Co-base superalloys machine-learning high-throughput method^[106]

Fig.15 Predicting results of the regression models including ordinary least square (OLS) (a), support vector regression (SVR) (b), artificial neural network (ANN) (c) and random forest (RF) (d)^[106]

Fig.16 Prediction performances on training dataset of the regression models in terms of the R and R² (a), MAE and RMSE (b) by 10-fold cross validation (Algorithms used are OLS, ANN, RF and support vector machine (SVM); R—the coefficient of correlation, R²—the explained variance, MAE—mean absolute error, RMSE—root mean squared error)^[106]

Fig.17 γ' solvus temperatures predicted by random forest regression (RFR) model OLS algorithm and DSC experimental test results of four new high temperature alloys^[106](a), and γ' solvus temperature comparision between 2Nb alloy^[106] and Co-8.8Al-9.8W-2X (X=Ti, V, Nb, Ta) alloy^[17] (b)

Fig.18 Microstructures of the designed Co-base superalloys by machine-learning high-throughput method (a~e)^[106]

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