Comment on “Machine learning enhanced analysis of EBSD data for texture representation”

Our concerns apply to the inadequate ways statistical distributions of crystallographic orientations are compared and occasionally confirmed to agree sufficiently well. The authors of “Machine learning enhanced analysis of EBSD data for texture representation” 1 suggest a method to replace an EBSD dataset of crystallographic orientations with a much smaller synthetic dataset preserving the texture. They claim that their “texture adaptive clustering and sampling” algorithm generates datasets of a few hundred crystallographic orientations, realizing an equivalent crystallographic orientation distribution as the initial dataset. To prove the principle and substantiate their claim of equivalent orientation distributions, the authors content themselves with (i) a visual inspection of the crystallographic pole density function, in fact, of three crystallographic “pole figures” and (ii) Kolmogorov–Smirnov tests for each of the three Euler angles of the crystallographic orientations individually. However, these criteria are insufficient to confirm equivalence of orientation distributions, they do not provide scientific evidence to substantiate the authors’ claim that “texture adaptive clustering and sampling” generates crystallographic orientations in terms of their Euler angles representing the same texture.