Fig. 7: Demonstration of our unsupervised ML method on grain identification of an IN100 Ni-based superalloy sample collected from serial-sectioning experiments.

a 3D input image reconstructed from electron backscatter diffraction (EBSD) data¹⁵ and the corresponding target grain segmentation labeled using inverse pole figure (IPF) coloring. In our method, the input image is pre-processed using a local variance filter and thresholding prior to the clustering and refinement step. b The predicted grain size distribution and grain segmentation obtained using our method. Boundary and unidentified voxels are colored by green and gray. c, d Lower resolution input images obtained by down sampling and the corresponding grain segmentation predicted by our method. The effect of down sampling is analogous to using a large bin size in the voxelization step for atomistic data. Down sampling significantly speeds up the processing but at the expense of accuracy (i.e., ability to detect small and fine features).