<p>Istraživanje u okviru doktorske disertacije obuhvata uzorkovanja inhalativne frakcije praškastih materija generisane u zubotehničkoj laboratoriji i procesom brušenja čelika EN 90MnCrV8, primenu savremenih metodologija uzorkovanja, analizu uzorka laserskom difrakcijom i analizom slike, zasnovanom na mikrografijama skenirajućeg elektronskog mikroskopa i statističku obradu podataka. Osnovni cilj istraživanja je razvoj funkcionalnog modela za evaluaciju rezultata merenja karakteristika praškastih materija, koji je obuhvatio identifikaciju, procenu i vrednovanje karakteristika prahova, u cilju unapređenja tačnosti rezultata u vezi sa karakterizacijom čestica. Složena geometrija čestica je ispitana analizom 14 parametara, 6 koji definišu veličinu i 8 parametara koji opisuju morfološke karakteristike, upotrebom dva softvera za analizu slike. Dobijeni rezultati obrađeni su primenom metode analize glavnih komponenti i hijerarhijske klaster analize, čime se prevazišao problem subjektivnog prilaza i izvršila redukcija i selekcije reprezantativnih parametara na osnovu kojih se može dovoljno dobro definisati i okarakterisati uzorak.</p> / <p>Тhis PhD thesis presented the development of the functional model for evaluation оf measuring results of powder materials’ characteristics, which involved the identification, assessment and evaluation of particles characteristics, in order to improve the accuracy of the results. Sampling of inhalation fraction of the powder materials’ was done in the dental laboratory and grinding process of the EN 90MnCrV8 steel. Particle analysis was performed whit laser diffraction and image analysis based on micrographics from scanning electron microscopy and data were statistically analyzed. The complex particle geometry was examined by analyzing 14 parameters, 6 which define the size and 8 parameters that describe the morphological characteristics, using two image analysis software. To deal with a large number of the numerical parameters used to characterise the particle geometry and the nonlinear relationships among these parameters, principal component analysis (PCA) and hierarchical cluster analysis (CA) algorithm was applied to identify representative numerical descriptors within each group.</p>
| Identifer | oai:union.ndltd.org:uns.ac.rs/oai:CRISUNS:(BISIS)107506 |
| Date | 13 September 2018 |
| Creators | Ilić Mićunović Milana |
| Contributors | Budak Igor, Vučinić-Vasić Milica, Hadžistević Miodrag, Štrbac Dragana, Kosec Borut, Vujić Bogdana |
| Publisher | Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, University of Novi Sad, Faculty of Technical Sciences at Novi Sad |
| Source Sets | University of Novi Sad |
| Language | Serbian |
| Detected Language | Unknown |
| Type | PhD thesis |
Page generated in 0.0017 seconds