| Abstract Scope |
Materials microstructures often contain multiple instances of a salient feature, and microstructural science involves quantifying these features individually and/or statistically. For example, using a computer vision approach, we can characterize a metal powder by analyzing each individual particle in an image. However, this analysis is challenged when particles touch or overlap. In this study, we take advantage of recent advances in deep learning to perform instance segmentation, in which individual segmentation masks are generated for each occurrence of a feature. For example, in an image of overlapping powder particles, instance segmentation allows individual particles to be extracted for further analysis. When combined with a machine learning classification scheme, we use this approach to measure the satellite content of powder samples, which is not possible with conventional powder characterization or image analysis techniques. This overall approach can be generalized to evaluate repetitive microstructural features across a range of structures. |