烧结陶瓷的计算显微结构分析
Computational Microstructure Analysis of Sintered Ceramics
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中文总结 AI 辅助
本研究提出自动化流程，结合拓扑滤波与Sauvola阈值处理，从烧结陶瓷的SEM图像提取显微结构特性，经1200℃、1400℃样品验证，IoU达95.14%、99.85%，可用于生成机器学习材料数据集。
中文摘要 AI 辅助
从显微结构图像中手动提取物理特性来表征材料是一项繁琐的工作。本研究提出了一种自动化流程，可从烧结陶瓷样品的扫描电子显微镜（SEM）图像中提取孔隙率、固相分数、晶粒尺寸分布和孔隙尺寸分布。从SEM图像提取物理特性的主要挑战在于，由于晶粒和孔隙相的强度范围重叠，SEM图像中会出现单峰直方图。我们评估了多种不同的SEM图像降噪和局部阈值处理方法，发现拓扑滤波结合Sauvola阈值处理能够实现SEM图像的分割和物理特性数据的提取。我们将自动化流程的结果与对1200℃和1400℃烧结样品进行手动分析的结果进行对比验证，分别获得了95.14%和99.85%的交并比（IoU）分数。该流程为从SEM图像自动提取显微结构特性提供了高效手段，是生成机器学习材料数据集的关键步骤。
英文摘要
Characterizing materials through manual extraction of physical properties from microstructure images is a laborious process. This work presents a workflow to extract porosity, solid fraction, grain size distribution, and pore size distribution from scanning electron microscopy (SEM) images of sintered ceramic samples using an automated pipeline. The primary challenge for extracting physical properties from SEM images is the presence of unimodal histograms in SEM images as a result of the overlapping intensity ranges for the grain and pore phases. We evaluated several different methods for noise reduction and local thresholding of SEM images. We find that topological filtering in combination with Sauvola thresholding enables segmentation and extraction of physical property data from SEM images. We validated the automated pipeline by comparing our results with the results of manual analyses performed for samples sintered at 1200$^o$C and 1400$^o$C and achieved an Intersection over Union (IoU) score of 95.14% and 99.85%, respectively. The workflow provides an efficient means to automatically extract microstructure properties from SEM images as a crucial step in generating materials datasets for machine learning.
