Understanding Structure-Processing Relationships in Metal Additive Manufacturing via Featurization of Microstructural Images

Abstract Understanding and predicting accurate property-structure-processing relationships for additively manufactured components is important for both forward and inverse design of robust, reliable parts and assemblies. While direct mapping of process parameters to properties is sometimes plausible, it is often rendered difficult due to poor microstructural control. Exploring the direct relationship between processing conditions and microstructural features can thus provide significant physical insights and aid the overall design process. Here, we develop an automated high-throughput framework to simulate an uncertainty-aware additive manufacturing (AM) process, characterize microstructural images, and extract meaningful features/descriptors. A kinetic Monte Carlo (KMC) model of the AM process is used as a digital twin to simulate microstructural evolution for a diverse set of experimentally relevant processing conditions. We perform a detailed parametric study to map the relationship between microstructural features and experimental conditions. Our results indicate that a many-to-one mapping can exist between processing conditions and typical descriptors. Multiple descriptors are thus necessary to unambiguously represent microstructural images. Our work provides crucial quantitative and qualitative information that would aid in the selection of features for microstructural images. Featurized microstructures could then be utilized to build data-driven models for predictive control of microstructures and thereby properties of additively manufactured components.