A Quantitative and Statistical Method of γ' Precipitates in Superalloy Based on the High-Throughput Field Emission Scanning Eelectron Microscope
LU Yuhua1,2, WANG Haizhou1,2(), LI Dongling1,2, FU Rui3, LI Fulin3, SHI Hui1,2
1Beijing Advanced Innovation Center for Materials Genome Engineering, Central Iron and Steel Research Institute, Beijing 100081, China 2Beijing Key Laboratory of Metal Materials Characterization, NCS Testing Technology Co., Ltd., Beijing 100081, China 3High Temperature Material Research Institute, Center Iron and Steel Research Institute, Beijing 100081, China
Cite this article:
LU Yuhua, WANG Haizhou, LI Dongling, FU Rui, LI Fulin, SHI Hui. A Quantitative and Statistical Method of γ' Precipitates in Superalloy Based on the High-Throughput Field Emission Scanning Eelectron Microscope. Acta Metall Sin, 2023, 59(7): 841-854.
Superalloys are used widely in national defense, energy, maritime, aviation, and other vital areas requiring stable and reliable materials owing to their excellent oxidation and heat corrosion resistance, high-temperature strength, good fatigue performance, and fracture toughness. The presence of a coherent gamma prime (γ') precipitate is the main factor affecting the high-temperature mechanical properties. Therefore, obtaining the quantitative and statistical γ' precipitate data is indispensable for examining and developing new superalloys. On the other hand, conventional instruments and methods barely achieve this goal. In this study, high-throughput field emission scanning electron microscope (high-throughput SEM) was introduced because of its high-speed imaging and original position visualization. Based on the high-throughput SEM, an innovative deformation GH4096 superalloy prepared at five different solution cooling rates were used as an object to establish a quantitative and statistical method for characterizing the primary, secondary, and tertiary γ' precipitates. Many images of γ' precipitates with magnifications of ×57000 and ×3000 were obtained rapidly, and methodologies for recognizing the γ' precipitates were developed using MIPAR software. Matrices of images of different amounts were formed. Through these methodologies, information on these matrices was obtained, including the ratio of the primary γ' precipitates area fractions between images with magnifications of ×57000 and ×3000. The ratio between the amounts of secondary and tertiary γ' precipitates and the area fraction of the secondary and tertiary γ' precipitates varied with the number of images investigated, respectively. By comparing the tendencies of these three results, the minimum field of view that could represent the actual distribution of γ' precipitates was set to a matrix of 13 × 13 images with a magnification of ×57000 and a pixels square of 2048 × 2048. Considering the consistency between the results of the standardized small-angle X-ray scattering (SAXS) and γ' precipitates in the 13 × 13 images, the established method was quantitative in characterizing the primary, secondary, and tertiary γ' precipitates of GH4096 superalloy. The results of the samples with five different solution cooling rates showed that the solution cooling rates strongly influenced the morphology and quantitative results of the γ' precipitates. Moreover, the behavior of the precipitates corresponded to the classical nucleation growth mechanism and Ostwald Ripening. The solution cooling rates influenced the tensile strength of the samples. The samples exhibited excellent tensile strengths at relatively faster cooling rates, more secondary γ' precipitates, and a higher total area fraction of secondary and tertiary γ' precipitates. Overall, a GH4096 superalloy was prepared using the established method. The statistical and quantitative results of the γ' precipitates highlight a novel way of studying the impact of the solution cooling process on γ' precipitates that can predict the performance of GH4096 superalloys.
Fig.1 Main procedures on building the Recipe-1 for characterizing primary γ' precipitates in SEM images with magnification of 3000
Fig.2 Original SEM images with a magnification of 3000, in which the black particles were primary γ' precipitates (a-c) and corresponding pseudo-colorized images by Recipe-1 according to the diameter of primary γ' precipitates (d-f) in sample CR-1
Sample
Number of SEM image
Average area fraction
1
2
3
4
5
CR-1
12.2256
11.4022
11.9496
11.0040
12.8140
11.8791
CR-2
11.4778
11.9671
12.0973
11.8957
11.8351
11.8546
CR-3
14.4065
14.4875
12.1480
12.8234
13.1789
13.4089
CR-4
14.4171
14.4254
15.6861
13.9458
12.9404
14.2830
CR-5
11.5034
11.1639
12.3995
12.7150
11.9160
11.9396
Table 1 Statistical area fractions of primary γ' precipitates in 5 pseudo-colorized images with magnification of 3000 in samples CR-1-CR-5
Fig.3 Main procedures on building the Recipe-2 for characterizing primary, secondary, and tertiary γ' precipitates in SEM images with magnification of 57000
Fig.4 Original SEM images with a magnification of 57000 (a-e) and corresponding pseudo-colorized images by Recipe-2 according to the diameter of secondary and tertiary γ' precipitates (f-j) and primary γ' precipitates (k-o) for samples CR-1 (a, f, k), CR-2 (b, g, l), CR-3 (c, h, m), CR-4 (d, i, n), and CR-5 (e, j, o)
Fig.5 Stitched image by 484 original SEM images with magnification of 57000 of sample CR-1 (The scanning matrix of images was 22 × 22)
Sample
MS
1 × 1
4 × 4
7 × 7
10 × 10
13 × 13
16 × 16
19 × 19
22 × 22
CR-1(A)
34.1182
14.1041
13.2905
13.2980
13.8856
13.4047
13.3321
13.3222
CR-1(B)
0
9.9570
10.8282
10.6772
10.0037
11.2758
11.1122
12.0587
CR-1(C)
18.2661
7.8074
17.8653
16.1729
14.5078
13.4821
12.9862
12.3973
CR-2
12.5951
15.3236
15.8086
15.5322
13.8407
13.2423
12.4115
12.5834
CR-3
19.1938
19.5960
17.8005
16.1286
15.9201
14.4578
14.2913
13.9391
CR-4
1.0355
21.3840
16.6870
15.5377
14.7993
13.9999
13.9489
14.4741
CR-5
0
9.5127
14.1524
14.7422
13.9434
13.9481
13.7549
13.8762
Table 2 Statistical area fractions of primary γ' precipitates in a series of different amounts of pseudo-colorized images with magnification of 57000 in samples CR-1-CR-5
Fig.6 Tendency of primary γ' precipitates area fractions ratio between images with magnification of 57000 and 3000 in all samples varied with the amount of image
Fig.7 Tendencies of the amount of secondary (a) and tertiary (b) γ' precipitates with magnification of 57000 in samples CR-1-CR-5 varied with the amount of image
Fig.8 Tendencies of the ratio between the amounts of secondary and tertiary γ' precipitates with magnification of 57000 in all samples varied with the investigated amount of image
Sample
MS
1 × 1
4 × 4
7 × 7
10 × 10
13 × 13
16 × 16
19 × 19
22 × 22
CR-1(A)
21.6772
35.7952
36.6602
36.6367
36.3409
36.3452
36.3276
36.1951
CR-1(B)
29.4490
31.6903
32.4940
33.0152
33.0311
32.4605
32.3384
31.6845
CR-1(C)
18.7779
30.1542
27.0086
27.6534
28.4279
29.0237
29.3381
29.4686
CR-2
28.7983
28.5878
28.9518
29.0544
29.5716
29.7345
29.9578
29.8297
CR-3
31.2861
36.7546
37.4507
38.3250
38.0414
38.6012
38.6170
38.4221
CR-4
25.6591
21.5179
23.8237
24.3587
24.8052
25.2127
25.2579
25.1590
CR-5
40.0036
37.3287
35.6865
34.6941
34.7349
34.4413
33.9881
33.3435
Table 3 Statistical area fractions of secondary γ' precipitates in a series of different amounts of pseudo-colorized images with magnification of 57000 in samples CR-1-CR-5
Sample
MS
1 × 1
4 × 4
7 × 7
10 × 10
13 × 13
16 × 16
19 × 19
22 × 22
CR-1(A)
4.3629
4.1475
3.9640
3.8927
4.0602
4.0986
4.1069
4.1726
CR-1(B)
4.1379
4.1596
4.1818
4.1152
4.1462
4.1376
4.1558
4.1437
CR-1(C)
3.4290
3.4859
3.7842
3.9601
3.9300
3.9189
3.8840
3.8672
CR-2
3.7788
3.5975
3.7266
3.6885
3.7277
3.7315
3.7312
3.7543
CR-3
5.9871
5.5649
5.3424
5.2784
5.2847
5.1110
5.0829
5.0549
CR-4
5.3514
5.9551
5.8162
5.7008
5.6556
5.6393
5.5932
5.5939
CR-5
8.5552
9.3975
9.2119
9.2089
9.2462
9.3367
9.4190
9.4513
Table 4 Statistical area fractions of tertiary γ' precipitates in a series of different amounts of pseudo-colorized images with magnification of 57000 in samples CR-1-CR-5
Fig.9 Original SEM image of CR-4 (a) and corresponding pseudo-colored recognitions only colored by Recipe-2 (b), and colored by Recipe-2 and manual work (c)
Fig.10 Comparisons of total amount (a), average diameter (b), and the median diameter (c) of secondary and tertiary γ' precipitates in 169 images with magnification of 57000 in samples CR-4 between Recipe-2 and Recipe-2 & manual, two ways to recognized γ' precipitates
Fig.11 Comparisons of mass fraction of γ' precipitates by using electrochemical extraction method & small-angle X-ray scattering (SAXS) (a, c, e, g, i) and area fraction of γ' precipitates by using the way in this work (b, d, f, h, j) in samples CR-1 (a, b), CR-2 (c, d), CR-3 (e, f), CR-4 (g, h), and CR-5 (i, j)
Fig.12 Mechanical properties of samples CR-1-CR-5 tensiled at 400oC (Rm—tensile strength, Rp0.2—yield strength (plastic deformation at 0.2%))
1
Reed R C. The Superalloys: Fundamentals and Applications [M]. Cambridge: Cambridge University Press, 2006: 1
2
Chen M S, Wang G Q, Li H B, et al. Annealing treatment methods and mechanisms for refining mixed and coarse grains in a solution treatment nickel-based superalloy [J]. Adv. Eng. Mater., 2019, 21: 1900558
doi: 10.1002/adem.v21.9
3
Guo J T. The current situation of application and development of superalloys in the fields of energy industry [J]. Acta Metall. Sin., 2010, 46: 513
doi: 10.3724/SP.J.1037.2009.00860
Zhang R, Liu P, Cui C Y, et al. Present research situation and prospect of hot working of cast & wrought superalloys for aero-engine turbine disk in China [J]. Acta Metall. Sin., 2021, 57: 1215
doi: 10.11900/0412.1961.2021.00153
Polkowska A, Polkowski W, Warmuzek M, et al. Microstructure and hardness evolution in Haynes 282 nickel-based superalloy during multi-variant aging heat treatment [J]. J. Mater. Eng. Perform., 2019, 28: 3844
doi: 10.1007/s11665-019-3886-0
6
Huang Q Y, Li H K. Superalloys [M]. Beijing: Metallurgical Industry Press, 2000: 24
黄乾尧, 李汉康. 高温合金 [M]. 北京: 冶金工业出版社, 2000: 24
7
Wu H Y, Huang Z W, Zhou N, et al. A study of solution cooling rate on γ′ precipitate and hardness of a polycrystalline Ni-based superalloy using a high-throughput methodology [J]. Mater. Sci. Eng., 2019, A739: 473
8
Mao J, Chang K M, Yang W H, et al. Cooling precipitation and strengthening study in powder metallurgy superalloy U720LI [J]. Metall. Mater. Trans., 2001, 32A: 2441
9
Lin D L, Yao D L, Lin X J, et al. Effect of volume fraction and size of fine γ′ on creep strength of a DS nickel-base superalloy [J]. Acta Metall. Sin., 1982, 18: 104
Chen Y Q, Prasath babu R, Slater T J A, et al. An investigation of diffusion-mediated cyclic coarsening and reversal coarsening in an advanced Ni-based superalloy [J]. Acta Mater., 2016, 110: 295
doi: 10.1016/j.actamat.2016.02.067
14
Singh A R P, Nag S, Hwang J Y, et al. Influence of cooling rate on the development of multiple generations of γ′ precipitates in a commercial nickel base superalloy [J]. Mater. Charact., 2011, 62: 878
doi: 10.1016/j.matchar.2011.06.002
15
Baldan A. Review progress in Ostwald ripening theories and their applications to nickel-base superalloys Part I: Ostwald ripening theories [J]. J. Mater. Sci., 2002, 37: 2171
doi: 10.1023/A:1015388912729
16
Dwarapureddy A K, Balikci E, Ibekwe S, et al. Activation energy for growth in single size distribution and the dissolution features of γ′ precipitates in the superalloy IN738LC [J]. J. Mater. Sci., 2008, 43: 1802
doi: 10.1007/s10853-007-2342-y
17
Hwang J Y, Banerjee R, Tiley J, et al. Nanoscale characterization of elemental partitioning between gamma and gamma prime phases in René 88 DT nickel-base superalloy [J]. Metall. Mater. Trans., 2009, 40A: 24
18
Hwang J Y, Nag S, Singh A R P, et al. Compositional variations between different generations of γ′ precipitates forming during continuous cooling of a commercial nickel-base superalloy [J]. Metall. Mater. Trans., 2009, 40A: 3059
19
Chen Y Q, Francis E, Robson J, et al. Compositional variations for small-scale gamma prime (γ′) precipitates formed at different cooling rates in an advanced Ni-based superalloy [J]. Acta Mater., 2015, 85: 199
doi: 10.1016/j.actamat.2014.11.009
20
Mao J, Chang K M, Yang W H, et al. Cooling precipitation and strengthening study in powder metallurgy superalloy Rene88DT [J]. Mater. Sci. Eng., 2002, A332: 318
21
Sharghi-Moshtaghin R, Asgari S. The influence of thermal exposure on the γ' precipitates characteristics and tensile behavior of superalloy IN-738LC [J]. J. Mater. Process. Technol., 2004, 147: 343
doi: 10.1016/j.jmatprotec.2004.01.006
22
Wang H Z, Wang H, Ding H, et al. Progress in high-throughput materials synthesis and characterization [J]. Sci. Technol. Rev., 2015, 33(10): 31
Wang H Z, Zhao L, Jia Y H, et al. State-of-the-art review of high-throughput statistical spatial-mapping characterization technology and its application [J]. Engineering, 2020, 6: 621
doi: 10.1016/j.eng.2020.05.005
24
Picó Y, Andreu V. Nanosensors and other techniques for detecting nanoparticles in the environment [A]. Nanosensors for Chemical and Biological Applications: Sensing with Nanotubes, Nanowires and Nanoparticles [M]. Amsterdam: Woodhead Publishing, 2014: 295
25
Yokoyama T, Masuda H, Suzuki M, et al. Basic properties and measuring methods of nanoparticles [A]. Nanoparticle Technology Handbook [M]. 2nd Ed., Amsterdam: Elsevier, 2012: 3
26
Smith T M, Bonacuse P, Sosa J, et al. A quantifiable and automated volume fraction characterization technique for secondary and tertiary γ' precipitates in Ni-based superalloys [J]. Mater. Charact., 2018, 140: 86
doi: 10.1016/j.matchar.2018.03.051
27
Chen N, Guo J J, Gong H F. The research progress of methods for characterizing nanoparticles [J]. Mod. Sci. Instrum., 2012, (2): 160
Smith T M, Senanayake N M, Sudbrack C K, et al. Characterization of nanoscale precipitates in superalloy 718 using high resolution SEM imaging [J]. Mater. Charact., 2019, 148: 178
doi: 10.1016/j.matchar.2018.12.018
29
Cao B, Chen M, Zhou Q. Comparison of three methods of measuring nano-particles size [J]. Phys. Examinat. Test., 2005, 23(6): 27
General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration. Nanometer powder—Determinaiton of particle size distribution—Small angle X-ray [S]. Beijing: Standards Press of China, 2005
Goertz V, Dingenouts N, Nirschl H. Comparison of nanometric particle size distributions as determined by SAXS, TEM and analytical ultracentrifuge [J]. Part. Part. Syst. Charact., 2009, 26: 17
doi: 10.1002/ppsc.200800002
32
Xiang Y W, Zhang J Y, Liu C L. Verification for particle size distribution of ultrafine powders by the SAXS method [J]. Mater. Charact., 2000, 44: 435
doi: 10.1016/S1044-5803(00)00061-9
33
Liu Q B, Lu C F, Yan P. Physicochemical phase analysis of a directionally solidified Ni-based cast superalloy [J]. Metall. Anal., 2006, 26(2): 9
Li L X, Zhao X L, Li N, et al. Physicochemical phase analysis of iron-nickel based high temperature alloy with high chromium [J]. Metall. Anal., 2015, 35(2): 6
Ju Y W, Li S, Yuan X F, et al. A macro-nano-atomic-scale high-throughput approach for material research [J]. Sci. Adv., 2021, 7: eabj8804
doi: 10.1126/sciadv.abj8804
36
Sosa J M, Huber D E, Welk B, et al. Development and application of MIPAR™: A novel software package for two- and three-dimensional microstructural characterization [J]. Integr. Mater. Manuf. Innov., 2014, 3: 123
doi: 10.1186/2193-9772-3-10
37
Fu R, Feng D, Chen X C, et al. Research of ESR-CDS technology [J]. J. Iron Steel Res., 2011, 23(suppl.2) : 1
Chen X C, Ren H, Fu R, et al. Recent development of solidified structure controlling of superalloy during ESR process [J]. Spec. Steel Technol., 2011, 17(3): 1
General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration. Particle size analysis—image analysis methods—part 1: Static image analysis method [S]. Beijing: Standards Press of China, 2008
Semiatin S L, Kim S L, Zhang F, et al. An investigation of high-temperature precipitation in powder-metallurgy, gamma/gamma-prime nickel-base superalloys [J]. Metall. Mater. Trans., 2015, 46A: 1715