Current efforts to modernize materials processing are hindered by a lack of fundamental, science-based understanding of the materials system (combination of feedstock material, production process, and finished part). A deeper understanding of the relationships between production process, material microstructure, and performance properties is the key to accelerating development timelines, achieving cost savings, and opening opportunities for game-changing materials systems for national security and nuclear energy. To address this challenge, an integrated methodology is being developed that brings together predictive modeling, inline monitoring, and data analytics. This methodology will enable high-fidelity in-silico experiments, efficient design of experiments, and process control which, taken together, will significantly accelerate development and qualification of materials systems. A key mechanism for integration within this effort is rigorous collection, curation, and sharing of data. This presentation will outline the methodology, highlight key advances in characterization, modeling, and ML, and demonstrate the key component of data management.