2, Texas A&M University, College Station, Texas, United States
Micrographs of materials contain microstructural information that is quantifiable in principle, but difficult to extract in practice. A quantitative and statistical understanding of microstructure features is necessary for establishing microstructure-property relationships, and especially helpful for nanometallic materials which have grain or domain dimensions less than 1 µm. We developed Automatic QUantitative Analysis of Microscopy Images (AQUAMI): an open source Python package that can automatically analyze micrographs of materials and extract quantitative information to characterize microstructure. In this presentation, we discuss the application of this digital image analysis method for obtaining essential nanoscale characteristics, such as the mean feature dimensions, size distribution, and phase area fraction while considering several potentially error causing experimental parameters. All measurements were fully objectively performed thus results are repeatable and can be directly compared between research groups, for example, taking images from published articles. We describe the working principle of the software and demonstrate it on micrographs of nanoporous and nanocomposite metals.