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dc.creatorVuksanović, Marija M.
dc.creatorGajić-Kvaščev, Maja
dc.creatorDojčinović, Marina
dc.creatorVolkov-Husović, Tatjana
dc.creatorJančić-Heinemann, Radmila
dc.date.accessioned2021-03-10T13:46:03Z
dc.date.available2021-03-10T13:46:03Z
dc.date.issued2018
dc.identifier.issn1044-5803
dc.identifier.urihttp://TechnoRep.tmf.bg.ac.rs/handle/123456789/3927
dc.description.abstractThe aim of this study was to examine the influence of cavitation erosion on the morphology of defects formed on the surfaces of alumina-based materials. The alumina-based samples were exposed to cavitation erosion for 42 min. The damages that occurred were monitored at predefined time intervals by measuring the mass loss and observing the sample surfaces. Digital images of surfaces were processed using an image analysis software package to define morphological characteristics of the damages occurred. The calculated morphological parameters were subjected to analysis by pattern recognition techniques to correlate the appearance of particular morphological characteristics with the behavior of the material during cavitation erosion. The newly established method in describing cavitation erosion which uses pattern recognition approach for treatments of morphological parameters calculated for damages by image analysis tools gave the results efficient for material characterization as the traditional one.en
dc.publisherElsevier Science Inc, New York
dc.relationinfo:eu-repo/grantAgreement/MESTD/Technological Development (TD or TR)/34011/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/45012/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Technological Development (TD or TR)/37021/RS//
dc.rightsrestrictedAccess
dc.sourceMaterials Characterization
dc.subjectCavitation erosionen
dc.subjectCeramic-matrix composite surface analysisen
dc.subjectErosion testingen
dc.subjectPattern recognitionen
dc.titleNew surface characterization tools for alumina based refractory material exposed to cavitation - Image analysis and pattern recognition approachen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage119
dc.citation.other144: 113-119
dc.citation.rankM21
dc.citation.spage113
dc.citation.volume144
dc.identifier.doi10.1016/j.matchar.2018.07.003
dc.identifier.scopus2-s2.0-85049600761
dc.identifier.wos000447477300012
dc.type.versionpublishedVersion


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