Utilizing the data assimilation and multi-phase-field grain growth model, this study proposes a novel framework of measuring anisotropic (nonuniform) grain boundary energy and mobility. The framework can evaluate a large number of boundary properties from typical observations of grain growth without requiring specifically designed experiments or calculations. In this method, by optimizing the multi-phase-field model parameters such that the simulation results are in good agreement with the observation data, the energies and mobilities of multiple individual boundaries are directly and simultaneously estimated. To validate the method, numerical tests on boundary property estimation were performed using synthetic microstructure dataset generated from grain growth simulations with a priori assumed property values. Systematic tests on simple tricrystal systems confirmed that the proposed method accurately estimates each boundary energy and mobility within an error of only several % of their assumed true values even for conditions with strong property anisotropy and grain rotation. Further numerical tests were conducted on a more general multi-grain system, showing that our method can be successfully applied to complicated polycrystalline grain growth. The obtained results demonstrate the potential of the proposed method in extracting a large dataset of grain boundary properties for arbitrary boundaries from actual grain growth observations.

本研究利用数据同化和多相场晶粒生长模型，提出了一种测量各向异性（非均匀）晶界能量和迁移率的新框架。该框架可以通过对晶粒生长的典型观察来评估大量边界特性，而无需专门设计的实验或计算。该方法通过优化多相场模型参数，使模拟结果与观测数据吻合良好，直接同时估计多个单独边界的能量和迁移率。为了验证该方法，使用从具有先验假设属性值的晶粒生长模拟生成的合成微观结构数据集对边界属性估计进行了数值测试。对简单三晶系统的系统测试证实，即使在具有强属性各向异性和晶粒旋转的条件下，所提出的方法也能准确估计每个边界能量和迁移率，误差仅为其假设真实值的几个 %。在更一般的多晶粒系统上进行了进一步的数值试验，表明我们的方法可以成功地应用于复杂的多晶晶粒生长。获得的结果证明了所提出的方法在从实际晶粒生长观察中提取任意边界的大量晶界特性数据集方面的潜力。