|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||ICME and ML Framework to Predict the Microstructure During U-10Mo Fuel Fabrication
||Ayoub Soulami, Yucheng Fu, William Frazier, Kyoo Sil Choi, Lei Li, Zhijie Xu, Curt Lavender, Vineet Joshi
|On-Site Speaker (Planned)
To reduce nuclear proliferation, low-enriched U-10Mo alloy has been identified as a promising fuel candidate for United States high-performance research reactors. During fabrication. Manufacturing the U-10Mo alloy involves a complex series of thermomechanical processing steps, including homogenization, hot rolling, annealing, cold rolling, and hot isostatic pressing. Several models/modeling methods have been developed for these individual processes. The interaction and coupling between individual processes use the concept of ICME, which aims to bridge the information passing between interacting models and investigates the impact of manufacturing processes on material microstructure evolution. Additionally, a Machine Learning (ML) model was developed and trained on both physics-based modeling and characterization data to predict the resultant microstructure after hot rolling passes and annealing. It is shown that implementing ICME leads to improved predictions, a better understanding of microstructure across multiple processes, and accelerated and more cost-effective development efforts.