Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model

Fu, Yucheng; Frazier, William E.; Choi, Kyoo Sil; Li, Lei; Xu, Zhijie; Joshi, Vineet V.; Soulami, Ayoub

doi:10.1038/s41598-022-14731-8

Title: Prediction of grain structure after thermomechanical processing of U-10Mo alloy using sensitivity analysis and machine learning surrogate model

Journal Article · Tue Jun 28 00:00:00 EDT 2022 · Scientific Reports

DOI:https://doi.org/10.1038/s41598-022-14731-8· OSTI ID:1874078

Fu, Yucheng; Frazier, William E.; Choi, Kyoo Sil; Li, Lei; Xu, Zhijie; Joshi, Vineet V.; Soulami, Ayoub

Abstract Hot rolling and annealing are critical intermediate steps for controlling microstructures and thickness variations when fabricating uranium alloyed with 10% molybdenum (U-10Mo), which is highly relevant to worldwide nuclear non-proliferation efforts. This work proposes a machine-learning surrogate model combined with sensitivity analysis to identify and predict U-10Mo microstructure development during thermomechanical processing. Over 200 simulations were collected using physics-based microstructure models covering a wide range of thermomechanical processing routes and initial alloy grain features. Based on the sensitivity analysis, we determined that an increase in rolling reduction percentage at each processing pass has the strongest effect in reducing the grain size. Multi-pass rolling and annealing can significantly improve recrystallization regardless of the reduction percentage. With a volume fraction below 2%, uranium carbide particles were found to have marginal effects on the average grain size and distribution. The proposed stratified stacking ensemble surrogate predicts the U-10Mo grain size with a mean square error four times smaller than a standard single deep neural network. At the same time, with a significant speedup (1000×) compared to the physics-based model, the machine learning surrogate shows good potential for U-10Mo fabrication process optimization.

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Research Organization:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC05–76RL01830; AC05-76RL01830

OSTI ID:: 1874078

Alternate ID(s):: OSTI ID: 1875700

Report Number(s):: PNNL-SA-170738; 10917; PII: 14731

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Vol. 12 Journal Issue: 1; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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