| Abstract Scope |
Neural network encoders were pre-trained on over 100,000 micrographs from NASA and the literature to learn robust microstructure representations. The pre-trained encoders were applied through transfer learning and individually fine-tuned to segment and extract features from micrographs of different materials. The extracted features were then linked to processing and/or property data in order to establish quantitative processing-structure-property relationships. The presentation will demonstrate the technique on several materials including: Ni-superalloys where precipitate morphology and matrix channel width are quantified, and environmental barrier coatings where a thermally grown oxide is segmented and its roughness, thickness, porosity, and inter-crack spacing are quantified and related to processing. |