Quantitative analysis of sintered NdFeB backscattered electron images based on a general large model

Abstract

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.