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A Bayesian Data Assimilation Framework for Characterizing Recrystallization in Steels
Abstract:
In this study, we develop a Bayesian data assimilation framework that combines a mean-field model of static recrystallization (MiReX) with a Sequential Importance Resampling (SIR) particle filter to estimate key material parameters from controlled synthetic experiments. MiReX, originally developed as a microstructurally based extension of Johnson–Mehl–Avrami–Kolmogorov kinetics, is used as a forward model in which the uncertain quantities include the grain-boundary mobility parameters (prefactor and activation energy), a stored-energy coefficient, an Avrami-type exponent, and an interface length scale. Synthetic recrystallized-fraction measurements are generated at two isothermal holding temperatures using a reference parameter set and are perturbed with Gaussian noise to mimic experimental uncertainty. Starting from broad uniform prior ranges, the particle filter propagates an ensemble of MiReX trajectories in time, updates particle weights using a Gaussian likelihood, and applies systematic resampling combined with Liu–West kernel regularization to reduce particle degeneracy while preserving posterior variance. The posterior obtained after assimilating the first temperature dataset is used as the prior for the second dataset, enabling sequential multi-temperature calibration. The synthetic experiments show that the framework recovers the reference parameters within credible intervals and provides tight uncertainty bounds on the predicted recrystallization kinetics. These results demonstrate that combining a physically based mean-field recrystallization model with sequential Monte Carlo methods provides a robust route for probabilistic parameter estimation and uncertainty quantification in microstructure evolution models.
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145-155
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April 2026
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