原文链接：https://doi.org/10.1007/s40789-023-00570-z或点击文末“阅读原文”
制约焦煤微观结构对其强度影响的基础研究的一个因素是焦煤微观结构的三维分类和分布方式难以量化。为了支持该基础研究内容，本研究评估了一种新的体积方法来分类小尺寸 (体积大约450 μm3) 焦煤块的三维计算机断层扫描显微结构，介绍了一种自动分类微结构模块的方法。它基于Landmark Multi-Dimensional Scaling，并使用Bhattacharyya度量和k-means聚类。使用27个不同性质的焦煤样品对该方法进行了检验，并根据与结构密度相关的体素强度分布可靠地确定了6个等级的焦煤微观结构。下层亚块体 (等级1-2) 以孔隙和薄壁为主。通常，在较高的等级中，壁厚增加，孔径减小。惰性特征也可能出现在更高等级 (等级5-6) 。总的来说，这种方法为识别焦炭CT扫描中微观结构的三维空间分布提供了一种有效的自动化手段。
Abstract One factor that limits development of fundamental research on the influence of coke microstructure on its strength is the difficulty in quantifying the way that microstructure is both classified and distributed in three dimensions. To support such fundamental studies, this study evaluated a novel volumetric approach for classifying small (approx. 450 μm3) blocks of coke microstructure from 3D computed tomography scans. An automated process for classifying microstructure blocks was described. It is based on Landmark Multi-Dimensional Scaling and uses the Bhattacharyya metric and k-means clustering. The approach was evaluated using 27 coke samples across a range of coke with different properties and reliably identified 6 ordered class of coke microstructure based on the distribution of voxel intensities associated with structural density. The lower class (1–2) subblocks tend to be dominated by pores and thin walls. Typically, there is an increase in wall thickness and reduced pore sizes in the higher classes. Inert features are also likely to be seen in higher classes (5–6). In general, this approach provides an efficient automated means for identifying the 3D spatial distribution of microstructure in CT scans of coke.
Fig. 1 An overview of the suggested process for characterising cokes based on the distribution of automatically classified blocks of microstructure. This study describes the classification step
Fig. 5 An overview of the unsupervised classification approach of coke structure
Fig. 14 Probability distribution of voxel intensities for each class of subblocks
引用格式：Nesbitt, K., Aziz, F., Mahoney, M. et al. Classifying coke using CT scans and landmark multidimensional scaling. Int J Coal Sci Technol 10, 7 (2023). https://doi.org/10.1007/s40789-023-00570-z
