  Grain growth competition during solidification determines microstructural
features, such as dendritic arm spacings, segregation pattern, and grain
texture, which have a key impact on the final mechanical properties. During
metal additive manufacturing (AM), these features are highly sensitive to
manufacturing conditions, such as laser power and scanning speed. The melt pool
(MP) geometry is also expected to have a strong influence on microstructure
selection. Here, taking advantage of a computationally efficient multi-GPU
implementation of a quantitative phase-field model, we use two-dimensional
cross-section simulations of a shrinking MP during metal AM, at the scale of
the full MP, in order to explore the resulting mechanisms of grain growth
competition and texture selection. We explore MPs of different aspect ratios,
different initial (substrate) grain densities, and repeat each simulation
several times with different random grain distributions and orientations along
the fusion line in order to obtain a statistically relevant picture of grain
texture selection mechanisms. Our results show a transition from a weak to a
strong $\langle10\rangle$ texture when the aspect ratio of the melt pool
deviates from unity. This is attributed to the shape and directions of thermal
gradients during solidification, and seems more pronounced in the case of wide
melt pools than in the case of a deeper one. The texture transition was not
found to notably depend upon the initial grain density along the fusion line
from which the melt pool solidifies epitaxially.
