On Ti6Al4V Microsegregation in Electron Beam Additive Manufacturing with Multiphase-Field Simulation Coupled with Thermodynamic Data

doi:10.1007/s40195-021-01318-x

Abstract

Abstract:

Electron beam additive manufacturing is an effective method for the fabrication of complex metallic components. With rapid solidification, the characteristics of microsegregation within the interdendritic region are interesting and important for the subsequent phase transformation and final mechanical properties. However, in view of the microsecond lifetime and the small length scale of the molten pool, experimentally investigating microsegregation is challenging, even with electron probe micro-analysis. In this study, a multiphase-field model coupled with the real thermodynamic data of Ti6Al4V alloy was successfully developed and applied to simulate the rapid solidification of columnar β grains via electron beam additive manufacturing. The thermal gradient (G) and cooling rate (R) were obtained from a 3D powder-scale multiphysics simulation and provided as inputs to a multiphase-field model. The effects of the electron beam process parameters and thermal conditions on the columnar β grains were investigated. Liquid films and droplets were observed to have solute enrichment in the intercellular region. The size of the liquid film increased at a lower scanning speed and energy power. Increasing the scanning speed and energy power refined the columnar β grains and decreased the liquid film size. The extent of microsegregation considerably increased at lower energy power, whereas the change in scanning speed had little effect on the microsegregation. The results also indicate that solute vanadium results in significant solute trapping and microsegregation during the rapid solidification of the Ti6Al4V alloy.

Key words: Ti6Al4V alloy, Microsegregation, Electron beam additive manufacturing, Multiphase-field model

Yujian Wang, Shuo Chu, Zhijun Wang, Junjie Li, Jincheng Wang. On Ti6Al4V Microsegregation in Electron Beam Additive Manufacturing with Multiphase-Field Simulation Coupled with Thermodynamic Data[J]. Acta Metallurgica Sinica (English Letters), 2022, 35(3): 425-438.

Figures/Tables 16

Fig. 1 Equilibrium concentrations of a Al, b V, c chemical-free energy of Ti6Al4V alloy at different temperatures

Table 1 Simulation parameters used in the multiphase-field model

Grid size dx (μm)	0.02
Time step dt (s)	1.491 × 10^-9
Initial composition of Al c₀₁ (at.%)	0.10198
Initial composition of V c₀₂ (at.%)	0.036
Interface width (μm) 2λ	0.1
Interface energyσ_SL (J/m²)	0.5
Anisotropy strength γ₄	0.02
Liquid diffusion coefficient of Al ${D}_{\mathrm{Al}}^{\mathrm{L}}$ (m²/s) [34]	1.24 × 10^-8
Liquid diffusion coefficient of V ${D}_{\mathrm{V}}^{\mathrm{L}}$ (m²/s) [34]	1.24 × 10^-8
Phase-field mobility for solid-liquid interface M_SL (m³/J/s)	6.6 × 10^-7
Phase-field mobility for solid-solid interface M_SS (m³/J/s)	6.6 × 10^-8

Fig. 2 a Two-dimensional cross-section of thermal field in the molten pool; b thermal characteristics of the black line in a at different time in the molten pool with the process parameters of P = 360 W; v = 0.5 m/s

Table 2 Thermal characteristics of molten pool with different process parameters

Process parameters	Thermal gradient ($\mathrm{K}/\mu m)$	Cooling rate ($\mathrm{K}/\mu s$)
P = 120 W; v = 0.5 m/s	1.07	0.10
P = 120 W; v = 0.25 m/s	0.59	0.05
P = 360 W; v = 0.5 m/s	0.85	0.14

Fig. 3 Dendrite evolution under the process parameter of P = 120 W; v = 0.25 m/s at different time

Fig. 4 Simulated steady-state columnar $\beta$ grains under different thermal conditions. The simulated results with dash line box corresponding to the process parameters listed in Table 2

Fig. 5 Variation of primary spacing with the growth velocity under different constant thermal gradients and the corresponding fitting results

Fig. 6 Simulated solute concentration profiles of a Al, b V

Fig. 7 Effect of thermal gradient on the solute distribution of a Al, b V in the intercellular regions while keeping a constant cooling rate of 0.05 K/μs

Fig. 8 Effect of cooling rate on the solute distribution of a Al, b V in the intercellular regions while keeping a constant thermal gradient of 0.85 K/μm

Fig. 9 Effect of process parameters on the solute distribution of a Al, b V in the intercellular regions

Fig. 10 Effect of process parameters and thermal conditions on the solute concentration of a Al, b V in the solid phase

Fig. 11 Comparison of tip concentration of a Al, b V under different process parameters

Table 3 Changes of tip concentration under different growth velocities. The bold contents from top to bottom correspond to the following process parameters: P = 120 W, v = 0.25 m/s; P = 120 W, v = 0.5 m/s; P = 360 W, v = 0.5 m/s

${\text{Velocity}}\,{ }\left( {{\mu m}/{\text{s}}} \right)$	$c_{{{\text{L}},{\text{tip}}}}^{{{\text{Al}}}}$	$c_{{{\text{S}},{\text{tip}}}}^{{{\text{Al}}}}$	$k_{{{\text{Al}}}}$	$c_{{{\text{L}},{\text{tip}}}}^{{\text{V}}}$	$c_{{{\text{S}},{\text{tip}}}}^{{\text{V}}}$	$k_{{\text{V}}}$
46,729	0.1034	0.1013	0.9796	0.0464	0.0313	0.6741
58,823	0.1034	0.1014	0.9806	0.0460	0.0320	0.6942
84,746	0.1033	0.1014	0.9816	0.0457	0.0321	0.7019
93,458	0.1033	0.1015	0.9825	0.0454	0.0328	0.7234
117,647	0.1032	0.1015	0.9835	0.0450	0.0328	0.7294
130,841	0.1031	0.1015	0.9844	0.0438	0.0331	0.7555
164,705	0.1030	0.1016	0.9864	0.0435	0.0334	0.7680
169,491	0.1030	0.1016	0.9864	0.0433	0.0335	0.7729
237,288	0.1029	0.1017	0.9883	0.0416	0.0341	0.8193

Fig. 12 Comparison of velocity-dependent partition coefficient of a Al, b V between the theoretical prediction of Eq. (19) and simulation results

Fig. 13 Solute segregation index of Al a, V b of the simulated columnar dendrites

References 43

[1]	C. Körner, Int. Mater. Rev. 61, 361 (2016) DOI URL
[2]	W.E. Frazier, J. Mater. Eng, Perform. 23, 1917 (2014)
[3]	S. Liu, Y.C. Shin, Mater. Des. 164, 107552(2019)
[4]	B. Wysocki, P. Maj, R. Sitek, J. Buhagiar, K.J. Kurzydłowski, W. Święszkowski, Appl. Sci. 7, 657 (2017) DOI URL
[5]	Y.L. Hao, S.J. Li, R. Yang, Rare Met. 35, 661 (2016) DOI URL
[6]	X.P. Tan, S. Chandra, Y. Kok, S.B. Tor, G. Seet, N.H. Loh, E. Liu, Materialia 7, 100365 (2019)
[7]	X. Gong, K. Chou, JOM 67, 1176 (2015)
[8]	S. Sahoo, K. Chou, Addit. Manuf. 9, 14 (2016)
[9]	V. Fallah, M. Amoorezaei, N. Provatas, S.F. Corbin, A. Khajepour, Acta Mater. 60, 1633 (2012) DOI URL
[10]	Y. Wang, J. Shi, Y. Liu, J. Cryst. Growth 521, 15 (2019)
[11]	X. Ao, H. Xia, J. Liu, Q. He, Mater. Des. 185, 108230(2020)
[12]	J. Kundin, L. Mushongera, H. Emmerich, Acta Mater. 95, 343 (2015) DOI URL
[13]	S. Chu, C. Guo, T. Zhang, Y. Wang, J. Li, Z. Wang, J. Wang, Y. Qian, H. Zhao, Eur. Phys. J. E 43, 35 (2020)
[14]	D. Liu, Y. Wang, Addit. Manuf. 25, 551 (2019)
[15]	S. Liu, Y.C. Shin, Comput. Mater. Sci. 183, 109889(2020)
[16]	Y. Wang, J. Li, H. Yang, J. Ma, D. Cui, Y. Gao, Z. Wang, J. Wang, Comput. Mater. Sci. 191, 110353(2021)
[17]	T. Pinomaa, N. Provatas, Acta Mater. 168, 167 (2019) DOI
[18]	T. Pinomaa, M. Lindroos, M. Walbrühl, N. Provatas, A. Laukkanen, Acta Mater. 184, 1 (2020) DOI URL
[19]	S. Kavousi, M. Asle Zaeem, Acta Mater. 205, 116562(2021)
[20]	T. Keller, G. Lindwall, S. Ghosh, L. Ma, B.M. Lane, F. Zhang, U.R. Kattner, E.A. Lass, J.C. Heigel, Y. Idell, M.E. Williams, A.J. Allen, J.E. Guyer, L.E. Levine, Acta Mater. 139, 244 (2017) DOI URL
[21]	V.B. Rajkumar, Y. Du, Y. Zeng, S. Tang, Calphad 68, 101691 (2020)
[22]	C. Yang, Q. Xu, B. Liu, J. Cryst. Growth 490, 25 (2018)
[23]	C. Yang, Q. Xu, X. Su, B. Liu, Acta Mater. 175, 286 (2019) DOI URL
[24]	D. Qiu, R. Shi, D. Zhang, W. Lu, Y. Wang, Acta Mater. 88, 218 (2015) DOI URL
[25]	D. Qiu, R. Shi, P. Zhao, D. Zhang, W. Lu, Y. Wang, Acta Mater. 112, 347 (2016) DOI URL
[26]	J. Li, Z. Wang, Y. Wang, J. Wang, Acta Mater. 60, 1478 (2012) DOI URL
[27]	I. Steinbach, F. Pezzolla, B. Nestler, M. Seeßelberg, R. Prieler, G.J. Schmitz, J.L.L. Rezende, Phys. D 94, 135 (1996)
[28]	J. Eiken, B. Boettger, I. Steinbach, Phys. Rev. E 73, 066122 (2006)
[29]	S.G. Kim, W.T. Kim, T. Suzuki, Phys. Rev. E 60, 7186 (1999)
[30]	S.G. Kim, Acta Mater. 55, 4391 (2007) DOI URL
[31]	S. Gyoon Kim, W. Tae Kim, T. Suzuki, M. Ode, J. Cryst. Growth 261, 135 (2004)
[32]	S.L. Chen, F. Zhang, S. Daniel, F.Y. Xie, X.Y. Yan, Y.A. Chang, R. Schmid-Fetzer, W.A. Oates, JOM 55, 48 (2003)
[33]	W. Sun, Y. Xie, R. Yan, S. Ma, H. Dong, T. Jing, Metall. Mater. Trans. B 50, 2487 (2019)
[34]	W. Sun, R. Yan, Y. Zhang, H. Dong, T. Jing, Comp. Mater. Sci. 160, 149 (2019) DOI URL
[35]	A. Ho, H. Zhao, J.W. Fellowes, F. Martina, A.E. Davis, P.B. Prangnell, Acta Mater. 166, 306 (2019) DOI URL
[36]	M.J. Bermingham, S.D. McDonald, D.H. StJohn, M.S. Dargusch, J. Mater. Res. 24, 1529 (2009) DOI URL
[37]	M.J. Bermingham, S.D. Mcdonald, M.S. Dargusch, D.H. Stjohn, J. Mater. Res. 23, 97 (2008) DOI URL
[38]	Y. Qian, W. Yan, F. Lin, Engineering 5, 746 (2019)
[39]	W. Yan, W. Ge, Y. Qian, S. Lin, B. Zhou, W.K. Liu, F. Lin, G.J. Wagner, Acta Mater. 134, 324 (2017) DOI URL
[40]	M.H. Burden, J.D. Hunt, J. Cryst. Growth 22, 99 (1974)
[41]	W. Kurz, D.J. Fisher, Acta Mater. 29, 11 (1981) DOI URL
[42]	M. Aziz, J. Appl. Phys. 53, 1158 (1982) DOI URL
[43]	T. Gong, Y. Chen, S. Li, Y. Cao, D. Li, X.Q. Chen, G. Reinhart, H. Nguyen-Thi, J. Mater. Sci. Technol. 74, 155 (2021) DOI URL