Machine learning based quantitative characterization of microstructures

Identifying structure-property relationships of polycrystalline microstructures demands an accurate and precise quantification of their features. Measuring grain sizes is tedious and creates a non-transparent bias when being performed by different individuals, impairing comparability with references. Here, we present a novel use of region-based convolutional neural networks (R-CNNs) to quantify several microstructural characteristics and their distributions: Feret diameter, axis length, area, circumference, dihedral angle and coordination number. We utilize a two-step approach: (i) a semi-automatic annotation tool to generate training data for (ii) a fully automated R-CNN, quantitatively evaluating images. Using Al-doped ZnO as a model system, we trained two R-CNNs, one for ZnO and one for precipitated ZnAl2O4. The R-CNN performs well in evaluating grain size characteristics from images with low contrast and in differentiating uni-, and bimodal grain size distributions on the sub-micron, and nanoscale. An extended statistical analysis of the distributions is performed to extract microstructural parameters quantitatively. This innovative solution makes grain size measuring amenable, time-effective, less biased, consistent, and statistically more precise.