Probing dendrite growth under microgravity via machine learning-aided multi-scale characterisation

Growth kinetics and orientation selection play a significant role in microstructure evolution during metal solidification, while gravity-induced convection adds significant complexity to the process. In-situ, time-resolved X-ray imaging of solidifying grain-refined Al–20 wt.% Cu alloy onboard the MASER-13 sounding rocket enabled the study of equiaxed dendrite growth under diffusion-controlled conditions, eliminating the influence of gravity. A machine learning-enabled analytical pipeline was developed to extract and evaluate the spatiotemporal behaviour of a large number of individual dendrites, including their growth characteristics, rotations and interactions. Post-flight synchrotron X-ray computed tomography and electron backscatter diffraction were used to reconstruct the three-dimensional dendrite structure with embedded details of crystallographic orientations. Correlated data analysis confirmed that most dendrites grew along directions parallel to the {100} plane under highly isothermal, diffusion-controlled conditions. However, growth along atypical directions was also observed, even in this simplified regime. The benchmark data revealed variation in dendrite arm evolution, influenced by local grain interactions and crystallographic orientation selection. It is shown that the equiaxed grains have random crystallographic orientations and evidence suggests that these survive from shortly after nucleation in the bulk liquid under microgravity conditions. The data processing protocols demonstrated here highlight the potential of integrating advanced experimental techniques with modern data science approaches to analyse solidification microstructure formation in metallic alloys under terrestrial and microgravity conditions.