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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.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Bayesian data assimilation, In situ, Phase-field simulation, Sintering, Nanoparticles

刊行年月日: 2024-08-02

出版者: Elsevier BV

掲載誌:

• Acta Materialia (ISSN: 13596454) vol. 278 120251

研究助成金:

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

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1016/j.actamat.2024.120251

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更新時刻: 2026-08-27 16:18:14 +0900

MDRでの公開時刻: 2026-08-27 18:27:15 +0900