High-Fidelity Phase-Field Simulation of Solid-State Sintering Enabled by Bayesian Data Assimilation Using In Situ Electron Tomography Data

Akimitsu Ishii; Akinori Yamanaka; Mizumo Yoshinaga; Shunsuke Sato; Midori Ikeuchi; Hikaru Saito; Satoshi Hata; Akiyasu Yamamoto

doi:10.1016/j.actamat.2024.120251

Journal article High-Fidelity Phase-Field Simulation of Solid-State Sintering Enabled by Bayesian Data Assimilation Using In Situ Electron Tomography Data

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High-fidelity phase-field simulation of solid-state sintering enabled by Bayesian data assimilation using in situ electron tomography data.pdf

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Akimitsu Ishii, Akinori Yamanaka, Mizumo Yoshinaga, Shunsuke Sato, Midori Ikeuchi, Hikaru Saito, Satoshi Hata, Akiyasu Yamamoto. High-Fidelity Phase-Field Simulation of Solid-State Sintering Enabled by Bayesian Data Assimilation Using In Situ Electron Tomography Data. Acta Materialia. 2024, 278 (), 120251. https://doi.org/10.1016/j.actamat.2024.120251

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Although the experimental characterization of the industrially important solid-state sintering is essential for the development of new materials and devices, it has never been performed in full, not least because of the limitations imposed by the complexity of target materials, experimental equipment, and observation conditions. Therefore, hybrid techniques for predicting sintering behavior based on experimental datasets and physics-based simulation models are highly sought after. Herein, a technique for constructing a digital twin of solid-state sintering is developed using a nonsequential Bayesian data assimilation (DA) method that integrates the experimental data obtained by in situ electron tomography/scanning transmission electron microscopy into the corresponding phase-field model and allows the inverse estimation of material parameters included therein. This technique is used to build a digital twin quantitatively capturing the solid-state sintering of copper nanoparticles and successfully estimate seven parameters (including temperature-dependent diffusion coefficients) from the time-series information on the morphology of sintered nanoparticles observed in situ. Thus, this study pioneers the establishment of digital twins for solid-state sintering based on DA-integrated phase-field simulations and real in situ observation datasets and deepens our understanding of the sintering process.

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Creative Commons BY Attribution 4.0 International

Keyword: Bayesian data assimilation, In situ, Phase-field simulation, Sintering, Nanoparticles

Date published: 2024-08-02

Publisher: Elsevier BV

Journal:

Acta Materialia (ISSN: 13596454) vol. 278 120251

Funding:

Japan Science and Technology Agency
Core Research for Evolutional Science and Technology
Japan Science and Technology Agency Strategic Basic Research Programs CREST JPMJCR18J4

Manuscript type: Publisher's version (Version of record)

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First published URL: https://doi.org/10.1016/j.actamat.2024.120251

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Updated at: 2026-08-27 16:18:14 +0900

Published on MDR: 2026-08-27 18:27:15 +0900

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