Jesbeer Kallungal, L. Chazeau, J. Chenal, J. Adrien, E. Maire, C. Barrès, B. Cantaloube, P. Heuillet
{"title":"基于x射线断层成像的弹性体复合材料中炭黑团聚体形态的数值聚类定量分析","authors":"Jesbeer Kallungal, L. Chazeau, J. Chenal, J. Adrien, E. Maire, C. Barrès, B. Cantaloube, P. Heuillet","doi":"10.5254/rct.22.77979","DOIUrl":null,"url":null,"abstract":"\n A novel methodology for characterizing the morphology distribution of filler agglomerates in elastomer composites is presented based on laboratory-sourced X-ray tomography. Various feature extraction methods (via, e.g., image-processing filters, segmentation) and selection tools (Spearman's rank correlation coefficient) combined with K-means unsupervised clustering algorithm were developed for identifying the distinct morphological classes in model materials (carbon-filled ethylene propylene diene monomer rubber). The interest of this methodology was demonstrated by precisely differentiating the materials compounded with different processing parameters. For instance, in this example, thanks to this analysis, it was found that introducing the filler before the elastomer in internal mixer tends to favor more structured agglomerates.","PeriodicalId":21349,"journal":{"name":"Rubber Chemistry and Technology","volume":" ","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"QUANTITATIVE ANALYSIS OF CARBON BLACK AGGLOMERATE MORPHOLOGY IN ELASTOMER COMPOSITES BASED ON X-RAY TOMOGRAPHY BY MEANS OF NUMERICAL CLUSTERING\",\"authors\":\"Jesbeer Kallungal, L. Chazeau, J. Chenal, J. Adrien, E. Maire, C. Barrès, B. Cantaloube, P. Heuillet\",\"doi\":\"10.5254/rct.22.77979\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n A novel methodology for characterizing the morphology distribution of filler agglomerates in elastomer composites is presented based on laboratory-sourced X-ray tomography. Various feature extraction methods (via, e.g., image-processing filters, segmentation) and selection tools (Spearman's rank correlation coefficient) combined with K-means unsupervised clustering algorithm were developed for identifying the distinct morphological classes in model materials (carbon-filled ethylene propylene diene monomer rubber). The interest of this methodology was demonstrated by precisely differentiating the materials compounded with different processing parameters. For instance, in this example, thanks to this analysis, it was found that introducing the filler before the elastomer in internal mixer tends to favor more structured agglomerates.\",\"PeriodicalId\":21349,\"journal\":{\"name\":\"Rubber Chemistry and Technology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2023-02-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Rubber Chemistry and Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.5254/rct.22.77979\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"POLYMER SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Rubber Chemistry and Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.5254/rct.22.77979","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"POLYMER SCIENCE","Score":null,"Total":0}
QUANTITATIVE ANALYSIS OF CARBON BLACK AGGLOMERATE MORPHOLOGY IN ELASTOMER COMPOSITES BASED ON X-RAY TOMOGRAPHY BY MEANS OF NUMERICAL CLUSTERING
A novel methodology for characterizing the morphology distribution of filler agglomerates in elastomer composites is presented based on laboratory-sourced X-ray tomography. Various feature extraction methods (via, e.g., image-processing filters, segmentation) and selection tools (Spearman's rank correlation coefficient) combined with K-means unsupervised clustering algorithm were developed for identifying the distinct morphological classes in model materials (carbon-filled ethylene propylene diene monomer rubber). The interest of this methodology was demonstrated by precisely differentiating the materials compounded with different processing parameters. For instance, in this example, thanks to this analysis, it was found that introducing the filler before the elastomer in internal mixer tends to favor more structured agglomerates.
期刊介绍:
The scope of RC&T covers:
-Chemistry and Properties-
Mechanics-
Materials Science-
Nanocomposites-
Biotechnology-
Rubber Recycling-
Green Technology-
Characterization and Simulation.
Published continuously since 1928, the journal provides the deepest archive of published research in the field. Rubber Chemistry & Technology is read by scientists and engineers in academia, industry and government.