Grain growth competition during solidification determines the microstructural features, such as primary and dendritic arm spacing, segregation pattern and grain texture, which have a key impact on the final mechanical properties. During metal additive manufacturing (MAM), these features are highly sensitive to manufacturing conditions, such as laser power and scanning speed. The melt pool (MP) geometry is expected to have a strong influence on microstructure selection, for instance exhibiting a transition from a 〈100〉 grain texture in the built direction for wide/shallow or deep/narrow MP to a 〈110〉texture for MP aspect ratio closer to unity (Jadhav, et al., Journal of Materials Processing Technology 270 (2019) 47-58). 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 MAM, at the scale of the full MP, in order to explore the resulting mechanisms of grain growth competition and texture selection. We explore MP of different aspect ratios 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.

Moderated by: Hani Henein / Mark Jolly