An automated and unbiased grain segmentation method based on directional reflectance microscopy

Abstract

Identifying individual grains from sectioned polycrystalline metals is a foundational task of microstructure analysis. However, traditional grain segmentation methods applied to optical micrographs may suffer from the lack of optical contrast between grains and require the manual selection of adjustable parameters to achieve acceptable segmentation results. We propose an alternative method which takes advantage of a multi-angle optical microscopy technique termed directional reflectance microscopy. By combining dimensionality reduction, similar-dissimilar classification, and multi-region merging of surface directional reflectance, our method enables fully automated and reliable grain segmentation of polycrystalline surfaces. We apply our method to metal samples with different crystal structures and grain orientation distributions. Our results suggest applicability of the method to a wide range of microstructures, enabling a more objective, robust, and universal characterization of polycrystalline metals.