Catania, Richard Knight2022-05-272022-05-272022-05-26vt_gsexam:34544http://hdl.handle.net/10919/110354This work explores physics-based and data-driven methods for material property prediction for metallic microstructures while indicating the context and benefit for microstructure- sensitive design. From this, the use of shape moment invariants is offered as solution to quantifying microstructure topology numerically using images. This offers a substantial benefit for computational time since image data is converted to numeric values. The goal of quantifying the image data is to help index grains based on their crystallographic orientation. Additionally, individual grains are isolated in order to investigate the effect of their shapes. After the microstructures are quantified, two methods for identifying the grain boundaries are proposed to make a more comprehensive approach to material property prediction. The grain boundaries as well as the grains of the quantified image are used to train artificial neural networks capable of predicting the material properties of the material. This prediction technique can be used as a tool for a microstructure-sensitive approach to design subtractively manufactured and Laser Engineered Net Shaping (LENS)-produced metallic materials.ETDenIn CopyrightMicrostructureGrain BoundaryImage ProcessingArtificial Neural NetworkThesis