New surface characterization tools for alumina based refractory material exposed to cavitation - Image analysis and pattern recognition approach
Само за регистроване кориснике
2018
Аутори
Vuksanović, Marija M.Gajić-Kvaščev, Maja
Dojčinović, Marina
Volkov-Husović, Tatjana
Jančić-Heinemann, Radmila
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
The 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 trad...itional one.
Кључне речи:
Cavitation erosion / Ceramic-matrix composite surface analysis / Erosion testing / Pattern recognitionИзвор:
Materials Characterization, 2018, 144, 113-119Издавач:
- Elsevier Science Inc, New York
Финансирање / пројекти:
- Развој опреме и процеса добијања полимерних композитних материјала са унапред дефинисаним функционалним својствима (RS-34011)
- Синтеза, процесирање и карактеризација наноструктурних материјала за примену у области енергије, механичког инжењерства, заштите животне стредине и биомедицине (RS-45012)
- Испитивање и верификација метода за мултидисциплинарне форензичке анализе у функцији непролиферације оружја за масовно уништење (RS-37021)
DOI: 10.1016/j.matchar.2018.07.003
ISSN: 1044-5803
WoS: 000447477300012
Scopus: 2-s2.0-85049600761
Институција/група
Tehnološko-metalurški fakultetTY - JOUR AU - Vuksanović, Marija M. AU - Gajić-Kvaščev, Maja AU - Dojčinović, Marina AU - Volkov-Husović, Tatjana AU - Jančić-Heinemann, Radmila PY - 2018 UR - http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3927 AB - The 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. PB - Elsevier Science Inc, New York T2 - Materials Characterization T1 - New surface characterization tools for alumina based refractory material exposed to cavitation - Image analysis and pattern recognition approach EP - 119 SP - 113 VL - 144 DO - 10.1016/j.matchar.2018.07.003 ER -
@article{ author = "Vuksanović, Marija M. and Gajić-Kvaščev, Maja and Dojčinović, Marina and Volkov-Husović, Tatjana and Jančić-Heinemann, Radmila", year = "2018", abstract = "The 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.", publisher = "Elsevier Science Inc, New York", journal = "Materials Characterization", title = "New surface characterization tools for alumina based refractory material exposed to cavitation - Image analysis and pattern recognition approach", pages = "119-113", volume = "144", doi = "10.1016/j.matchar.2018.07.003" }
Vuksanović, M. M., Gajić-Kvaščev, M., Dojčinović, M., Volkov-Husović, T.,& Jančić-Heinemann, R.. (2018). New surface characterization tools for alumina based refractory material exposed to cavitation - Image analysis and pattern recognition approach. in Materials Characterization Elsevier Science Inc, New York., 144, 113-119. https://doi.org/10.1016/j.matchar.2018.07.003
Vuksanović MM, Gajić-Kvaščev M, Dojčinović M, Volkov-Husović T, Jančić-Heinemann R. New surface characterization tools for alumina based refractory material exposed to cavitation - Image analysis and pattern recognition approach. in Materials Characterization. 2018;144:113-119. doi:10.1016/j.matchar.2018.07.003 .
Vuksanović, Marija M., Gajić-Kvaščev, Maja, Dojčinović, Marina, Volkov-Husović, Tatjana, Jančić-Heinemann, Radmila, "New surface characterization tools for alumina based refractory material exposed to cavitation - Image analysis and pattern recognition approach" in Materials Characterization, 144 (2018):113-119, https://doi.org/10.1016/j.matchar.2018.07.003 . .