Experimental observation methods for understanding industrially important solid-state sintering are essential for the development of new materials and devices. To experimentally characterize solid-state sintering, the limitations posed by the complexity of target materials, experimental equipment, and observation conditions must be overcome. Therefore, hybrid techniques for predicting sintering behavior based on experimental datasets and physics-based simulation models are highly sought after. Herein, we propose a new technique for combining a physics-based model and experimental observation results from solid-state sintering using a nonsequential Bayesian data assimilation (DA) method. The proposed technique assimilates experimental data—obtained using electron tomography/scanning transmission electron microscopy—into the corresponding phase-field (PF) model to enable high-fidelity PF simulations by estimating multiple material parameters included in the PF model. This study demonstrates the inverse estimation of seven parameters, including temperature-dependent diffusion coefficients, from the time-series information on the morphology of sintered nanoparticles observed . The estimated parameters provide the high-fidelity PF simulation to capture the observed solid-state sintering of copper nanoparticles. Thus, this study contributes to the construction of digital twins for solid-state sintering based on DA-integrated PF simulations and observation datasets and deepens our understanding of the sintering process.

中文翻译：

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使用原位电子断层扫描数据通过贝叶斯数据同化实现固态烧结的高保真相场模拟


用于了解工业上重要的固态烧结的实验观察方法对于新材料和设备的开发至关重要。为了通过实验表征固态烧结，必须克服靶材料、实验设备和观察条件的复杂性所带来的限制。因此，基于实验数据集和基于物理的模拟模型来预测烧结行为的混合技术受到高度追捧。在此，我们提出了一种新技术，使用非序列贝叶斯数据同化（DA）方法将基于物理的模型和固态烧结的实验观察结果相结合。所提出的技术将使用电子断层扫描/扫描透射电子显微镜获得的实验数据同化到相应的相场 (PF) 模型中，通过估计 PF 模型中包含的多个材料参数来实现高保真 PF 模拟。这项研究证明了从观察到的烧结纳米颗粒形态的时间序列信息对七个参数（包括温度相关扩散系数）的逆估计。估计的参数提供高保真 PF 模拟，以捕获观察到的铜纳米粒子的固态烧结。因此，这项研究有助于构建基于DA集成PF模拟和观测数据集的固态烧结数字孪生，并加深我们对烧结过程的理解。