Modeling of microstructure and microsegregation evolution in solidification of ternary alloys
Qiannan Yu(于芊楠)1,2,3, Mengdan Hu(胡梦丹)1,2,3, Jinyi Wu(吴津仪)1,2,3, and Dongke Sun(孙东科)1,2,3,†
1 School of Mechanical Engineering, Southeast University, Nanjing 211189, China; 2 Jiangsu Key Laboratory for Biomaterials and Devices, Southeast University, Nanjing 211189, China; 3 Key Laboratory of Structure and Thermal Protection of High Speed Aircraft, Ministry of Education, School of Mechanical Engineering, Southeast University, Nanjing 211189, China
Abstract The microstructure formed during solidification has a significant impact on the mechanical properties of materials. In this study, a two-dimensional (2D) cellular automaton (CA)-finite difference (FD)-CALPHAD model was developed to simulate the formation of microstructure and solute segregation in the solidification processes of ternary alloys. In the model, dendritic growth is simulated using the CA technique, while solute diffusion is solved by the FD method, and the CALPHAD method is employed to calculate thermodynamic phase equilibrium during solidification. The CA-FD-CALPHAD coupled model is capable of reproducing the evolution of continuous nucleation and growth of grains as well as the evolution of the microstructure and solute distribution during solidification of ternary alloys. In this study, Al-Zn-Mg ternary alloy is taken as an example to simulate the growth of equiaxed and columnar grains and the columnar-to-equiaxed transition (CET) under different solidification conditions. The simulation results are compared with experimental data from the literature, showing a good agreement. Besides, the study also investigates the evolution of temperature and multicomponent solute fields during solidification and the effects of alloy composition and cooling rate on the microstructure morphology. The results reveal that the initial alloy composition and cooling rate significantly affect dendritic morphology and solute segregation. Higher initial alloy concentrations promote the growth of side branches in equiaxed grains, leading to more pronounced solute segregation between dendrites. As the cooling rate increases, the average grain size of the equiaxed grains decreases accordingly. Additionally, a higher cooling rate accelerates the columnar-to-equiaxed transition, leading to a finer grain structure.
(Methods of crystal growth; physics and chemistry of crystal growth, crystal morphology, and orientation)
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 52301035), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20230844), and the National Key Research and Development Program of China (Grant No. 2023YFB3710202).
Corresponding Authors:
Dongke Sun
E-mail: dksun@seu.edu.cn
Cite this article:
Qiannan Yu(于芊楠), Mengdan Hu(胡梦丹), Jinyi Wu(吴津仪), and Dongke Sun(孙东科) Modeling of microstructure and microsegregation evolution in solidification of ternary alloys 2025 Chin. Phys. B 34 056801
[1] Hong C P and Zhu M F 2001 ISIJ Int. 41 43 [2] Paliwal M, Kang D H, Essadiqi E and Jung I 2014 Metall. Mater. Trans. A. Phys. Metall. Mater. Sci 45 3596 [3] Martorano M A and Biscuola V B 2009 Acta. Mater. 57 607 [4] Yan X, Chen S, Xie F and Chang Y A 2002 Acta Materialia 50 2199 [5] Zepon G, Ellendt N, Uhlenwinkel V and Bolfarini C 2016 Metall. Mater. Trans. A Phys. Metall Mater. Sci. 47 842 [6] Jia Y W, Huang H J, Fu Y A, Zhu G L, Shu D, Sun B D and StJohn D H 2019 Scr. Mater. 167 6 [7] Delaleau P, Beckermann C, Mathiesen R H and Arnberg L 2010 ISIJ Int. 50 1886 [8] Nguyen-Thi H, Reinhart G, Mangelinck-Noel N, Jung H, Billia B, Schenk T, Gastaldi J, Hartwig J and Baruchel J 2007 Metall. Mater. Trans. A Phys. Metall. Mater. Sci. 38A 1458 [9] Nestler B and Wheeler A 2000 Physica D 138 114 [10] Zhang A, YangMH, Qin L, Cheng J, Tang Y C, Du J L, YuWB, Dong Z H, Liu F, Jiang B and Pan F S 2024 Comput. Math. Appl. 176 289 [11] Zhu C S, Wang T, Feng L, Lei P and Ma F L 2022 Chin. Phys. B 31 028102 [12] Mao S L, HuMD, ChenWand Sun D K 2024 Mater. Des. 246 113362 [13] Zhang A, Guo Z P, Jiang B, Du J L, Wang C H, Huang G S, Zhang D F, Liu F, Xiong S M and Pan F S 2021 Acta Mater. 214 117005 [14] Eshraghi M, Hashemi M, Jelinek B and Felicelli S D 2017 Metals 7 474 [15] An D, Chen S L, Sun D K Pan S Y, Krakauer B W and Zhu M F 2019 Comput. Mater. Sci. 166 210 [16] Zhang S J, Zhu B F, Li Y B, Zhang Y and Li R 2024 Comput. Mater. Sci. 245 113308 [17] Zhang Y J, Zhou J X, Yin Y J, Shen X and Ji X Y 2021 J. Mater. Res. Technol. 14 1862 [18] Tan Y X, Zhao H D and Xu Q Y 2024 Sci. China Mater. 67 1150 [19] Gu C, Ridgeway C D, Moodispaw, M P and Luo A A 2020 J. Mater. Process. Technol. 286 116829 [20] Liao S H, Webster S, Huang D, Council R, Ehmann K and Cao J 2022 Addit. Manuf. 56 102912 [21] Ramirez A J and Lippold J C 2004 Mater. Sci. Eng. A 380 245 [22] Cook P S and Murphy A B 2020 Addit. Manuf. 31 100909 [23] Chen H R, Liu X, Liu X C and Witherell P 2024 Addit. Manuf. 84 104095 [24] Sheng Y W, Zhou Z Q, Meng X N and Ru J S 2024 Can. Metall. Q 0 1 [25] Xiong F Y, Liang Y P, Panwisawas C, Chen J W, Li M J and Liu A W 2024 Addit. Manuf. 95 104517 [26] An Z, Pan J J, Hu X F and Yang H Y 2025 Int. J. Heat Mass Transfer 238 126452 [27] Chen S L, Daniel S, Zhang F Chang Y A, Yang X Y, Xie F Y, Schmid- Fetzer R and Oates W A 2002 Calphad 26 175 [28] CaoW, Chen S L, Zhang F,Wu K, Yang Y, Chang Y A, Schmid-Fetzer R and Oates W A 2009 Calphad 33 328 [29] Fang H, Tang Q Y, Zhang Q Y, Gu T F and Zhu M F 2019 Int. J. Heat Mass Transfer 133 371 [30] Pan S Y and Zhu M F 2010 Acta Mater. 58 340 [31] Boettinger W J, Warren J A, Beckermann C and Karma A 2002 Annu. Rev. Mater. Sci. 32 163 [32] Hu M D, Sun D K and Zhu M F 2024 Mater. Des. 241 112977 [33] Beltran-Sanchez L and Stefanescu D M 2004 Metall. Mater. Trans. A Phys. Metall. Mater. Sci. 35 2471 [34] Liu T T, Mu Z Y, Hu R Z and Pang S Y 2019 Int. J. Heat Mass Transfer 140 346 [35] Tan C W, Dong Y, Sun H F, Liu F Y, Han X, Dong Q J, Chen B and Song X G 2023 J. Mater. Res. Technol. 26 6446 [36] Zhu M F, Kim J M and Hong C P 2001 ISIJ Int. 41 992 [37] Marsh S P and Glicksman M E 1996 Metall. Mater. Trans. A Phys. Metall. Mater. Sci. 27 557 [38] Spinelli J E, Peres M D and Garcia A 2005 J. Alloys Compd. 403 228 [39] Zhu M F and Hong C P 2001 ISIJ Int. 41 436 [40] Wu M, Li J, Ludwig A and Kharicha A 2013 Comput. Mater. Sci. 79 830 [41] Wu M H, Ludwig A, Bührig-Polaczek A, Fehlbier M and Sahm P R 2003 Int. J. Heat Mass Transfer 46 2819 [42] Wang J L, Wang F M, Zhao Y Y, Zhang J M and Ren W 2009 Int. J. Miner. Metall. Mater. 16 640 [43] Xiong L D, Zhu G L, Mi G Y, Wang C M and Jiang P 2021 J. Alloys Compd. 858 157669 [44] Lu B, Li Y, Wang H Y, Wang Y, Yu W, Wang Z D and Xu G M 2023 J. Mater. Res. Technol. 22 2532
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