Automated characterization and classification of 3D microstructures: an application to 3D deformation twin networks in titanium

Abstract

The advent of techniques enabling three-dimensional (3D) analysis of objects, defects, and fields has been key to discoveries and paradigm shifts in molecular biology, astrophysics, medicine, quantum physics, etc. In materials science, the 3D nature of materials microstructures remains largely hidden; leading to a fragmented understanding of microstructure-property linkages. Current tools cannot characterize large volumes of 3D microstructures at fine resolution. To this end, this study introduces a graph-theory-based framework to automatically extract 3D microstructures and statistics of electron-backscatter diffraction datasets. Further, leveraging network science, the study introduces a new approach to classify and compare microstructures; the keystone to materials taxonomy. The significance of this tool is demonstrated by studying deformation twin structures in Titanium. The study reveals extraordinarily complex and tortuous twin networks never observed via traditional two-dimensional analysis. This changes our perception of the ability of metals to withstand severe microstructure changes without failing.