Classifying coke using CT scans and landmark multidimensional scaling

作     者：Keith Nesbitt Fayeem Aziz Merrick Mahoney Stephan Chalup Bishnu P.Lamichhane Keith Nesbitt;Fayeem Aziz;Merrick Mahoney;Stephan Chalup;Bishnu P.Lamichhane

作者机构：School of Information and Physical SciencesUniversityof NewcastleCallaghanNSWAustralia 

出 版 物：《International Journal of Coal Science & Technology》 (国际煤炭科学技术学报（英文）)

年 卷 期：2023年第10卷第1期

页      面：160-172页

核心收录：

学科分类：0808[工学-电气工程] 07[理学] 0807[工学-动力工程及工程热物理] 0818[工学-地质资源与地质工程] 0815[工学-水利工程] 0813[工学-建筑学] 0814[工学-土木工程] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：the Australian Coal Industry's Research Program(ACARP)-Grant Number C29073 

主　　题：Coke Microstructure Clustering Classifcation Computer tomography 

摘      要：One factor that limits development of fundamental research on the infuence of coke microstructure on its strength is the difculty in quantifying the way that microstructure is both classifed and distributed in three *** support such fundamental studies,this study evaluated a novel volumetric approach for classifying small(approx.450μm^(3))blocks of coke microstructure from 3D computed tomography *** automated process for classifying microstructure blocks was *** is based on Landmark Multi-Dimensional Scaling and uses the Bhattacharyya metric and k-means *** approach was evaluated using 27 coke samples across a range of coke with diferent properties and reliably identifed 6 ordered class of coke microstructure based on the distribution of voxel intensities associated with structural *** lower class(1–2)subblocks tend to be dominated by pores and thin ***,there is an increase in wall thickness and reduced pore sizes in the higher *** features are also likely to be seen in higher classes(5–6).In general,this approach provides an efcient automated means for identifying the 3D spatial distribution of microstructure in CT scans of coke.