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Phase field investigation of dendrite growth in the welding pool of aluminum alloy 2A14 under transient conditions AITranslate

Harbin Institute of Technology; Harbin Institute of Technology; Nanjing University of Aeronautics and Astronautics; Harbin Institute of Technology; Harbin Institute of Technology;Shanghai Space Propulsion Technology Research Institute
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Publisher: Elsevier
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Abstract AITranslate

Highlights • The relationships between the critical parameters and the macro variables are built. • The transient welding conditions are applied to the dendrite growth simulation. • We obtain the evolutions of concentration and morphology. • The evolution of the tip velocity is obtained in the solidification of welding pool. • Primary arm spacing in simulation result agrees well with that in experiment result. A phase field (PF) model is developed to investigate the microstructure evolution near the fusion line during solidification of the gas tungsten arc welding (GTAW) pool for aluminum alloy 2A14. The equations of temperature gradient and pulling speed are employed to consider the transient conditions of the welding pool. Time evolutions of the dendrite growth and tip velocity are obtained. The dendrite growth can be divided into the planar growth stage, competitive growth stage and short-term steady growth stage. The trends of tip velocity are distinctly different with that of the pulling speed in different stages. Thermal undercooling and constitutional undercooling have different dominating effects on the tip velocity, which results in the fact that the time point when the tip velocity reaches its peak value is inconsistent with that of both thermal undercooling and constitutional undercooling. For the given welding parameters, the average primary dendrite arm spacing obtained by the simulation agrees well with the experimental results. Moreover, during the simulation, the side branches grow at an angle with a velocity component perpendicular to the primary dendrite orientation, which is consistent with the experiments.

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DOI:https://doi.org/10.1016/j.commatsci.2013.08.022

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Citation Information:

Highlights • The relationships between the critical parameters and the macro variables are built. • The transient welding conditions are applied to the dendrite growth simulation. • We obtain the evolutions of concentration and morphology. • The evolution of the tip velocity is obtained in the solidification of welding pool. • Primary arm spacing in simulation result agrees well with that in experiment result. A phase field (PF) model is developed to investigate the microstructure evolution near the fusion line during solidification of the gas tungsten arc welding (GTAW) pool for aluminum alloy 2A14. The equations of temperature gradient and pulling speed are employed to consider the transient conditions of the welding pool. Time evolutions of the dendrite growth and tip velocity are obtained. The dendrite growth can be divided into the planar growth stage, competitive growth stage and short-term steady growth stage. The trends of tip velocity are distinctly different with that of the pulling speed in different stages. Thermal undercooling and constitutional undercooling have different dominating effects on the tip velocity, which results in the fact that the time point when the tip velocity reaches its peak value is inconsistent with that of both thermal undercooling and constitutional undercooling. For the given welding parameters, the average primary dendrite arm spacing obtained by the simulation agrees well with the experimental results. Moreover, during the simulation, the side branches grow at an angle with a velocity component perpendicular to the primary dendrite orientation, which is consistent with the experiments.

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GB/T 7714-2015 [1] W.J. Zheng, Z.B. Dong, Y.H. Wei, et al. Computational Materials Science, 2014(82). DOI:10.1016/j.commatsci.2013.08.022.
MLA [1] W.J. Zheng, et al., Computational Materials Science, no. 82, 2014, https://doi.org/10.1016/j.commatsci.2013.08.022.
APA [1] W.J. Zheng, Z.B. Dong, Y.H. Wei, K.J. Song, J.L. Guo, & Y. Wang. (2014). Computational Materials Science(82). https://doi.org/10.1016/j.commatsci.2013.08.022
IEEE [1] W.J. Zheng, Z.B. Dong, Y.H. Wei, K.J. Song, J.L. Guo, and Y. Wang, Computational Materials Science, no. 82, 2014, doi: 10.1016/j.commatsci.2013.08.022.