The macroscopic performance of magnets is determined by their microscopic structure, quantifying the microscopic image of magnets is of great importance for studying its performance. Backscattered electron images of sintered NdFeB magnets contain information about the size, morphology, and distribution of grains and the grain boundary phases. Traditional methods for quantifying images involve labor-intensive manual measurements, digital image processing with complex contour extraction algorithms, and convolutional neural network algorithms that require extensive image data labeling. In this study, we introduced a general vision large model called Segment Anything Model(SAM) for image segmentation. SAM enables rapid and accurate segmentation of grains and grain boundary phases without the need for complex algorithms and tedious data labeling. From the segmented mask images, we extracted various data related to the performance of magnets, including the centroid positions, perimeter, area, sphericity, roughness, and principal axis directions of all grains. We also obtained information on the distances and angles between adjacent grains and the relevant parameters affecting magnet performance, such as the number and volume of grain boundary phases. We conducted comprehensive quantification of the backscattered images for three different magnets and provided reasonable explanations for the differences in magnet performance. This model offers superior speed and accuracy in image quantification compared to traditional algorithms and can be used for the rapid analysis of large datasets. It represents an essential method and trend for the quantification of image data in the future.

中文翻译：

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基于通用大模型的烧结NdFeB背散射电子图像定量分析

磁体的宏观性能由其微观结构决定，量化磁体的微观图像对于研究其性能具有重要意义。烧结 NdFeB 磁体的背散射​​电子图像包含有关晶粒和晶界相的尺寸、形态和分布的信息。量化图像的传统方法涉及劳动密集型的手动测量、具有复杂轮廓提取算法的数字图像处理以及需要大量图像数据标记的卷积神经网络算法。在本研究中，我们引入了一种通用视觉大型模型，称为分段任意模型（SAM），用于图像分割。 SAM 可以快速准确地分割晶粒和晶界相，无需复杂的算法和繁琐的数据标记。从分割的掩模图像中，我们提取了与磁体性能相关的各种数据，包括所有晶粒的质心位置、周长、面积、球形度、粗糙度和主轴方向。我们还获得了相邻晶粒之间的距离和角度以及影响磁体性能的相关参数的信息，例如晶界相的数量和体积。我们对三种不同磁体的后向散射图像进行了全面量化，并对磁体性能的差异提供了合理的解释。与传统算法相比，该模型在图像量化方面提供了卓越的速度和准确性，可用于大型数据集的快速分析。它代表了未来图像数据量化的重要方法和趋势。