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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 Xingjun1,2,3(),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
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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)
Corresponding Authors:  Xingjun LIU,Cuiping WANG     E-mail:;

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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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 (ΔEd) 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]
SortSystemElement X

Binary system

Co-XNi, Al, W, Ta, Ti, Cr
Ni-XAl, W, Ta, Ti, Cr
Al-XW, Ta, Ti, Cr
W-XTa, Ti, Cr
Ta-XTi, Cr

Ternary system

Co-Al-W, Ni-Al-Co, Ni-Al-W, Ni-Al-Ta, Ni-Co-W, Ni-Ti-Ta, Ni-Ti-Cr,

Co-Ni-W, Co-Ta-Cr, Co-Ta-Ni

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?ii) (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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