Hlavní stránka > Výsledky hledání: Materna, Jiří
Hledej:
Tipy pro vyhledávaní :: Rozšířené hledání
Hledej ve sbírkách:
Seřadit podle: Zobrazit výsledky: Výstupní formát:
Národní úložiště šedé literatury Nalezeno 2 záznamů.  Hledání trvalo 0.01 vteřin. 
Microstructural classification of multiphase steels by advanced microscopy and image analysis techniques
 Jozefovič, Patrik ; Materna, Jiří (oponent) ; Mikmeková, Šárka (vedoucí práce)
Austenitic stainless steels have found application in various sectors due to their characteristic properties. Metastable character of some of them, which allows martensitic transformation, is connected with possible risks in form of decrease of toughness. For revelation of martensitic phase in a microstructure of the austenitic stainless steel, techniques such as electron backscatter diffraction (EBSD) in scanning electron microscope (SEM) are used. However, the EBSD is time-consuming and it requires high quality of metallographic specimen preparation. Goal of this thesis is to find other techniques, which allow separation of the phases in metastable austenitic stainless steel in the SEM, as well as optimization of metallographic specimen preparation for needs of the SEM. After fulfilment of these goals, the thesis focuses on possibility of usage of the so-called deep learning for purpose of automated separation of phases in micrographs from the SEM. For this purpose, 4 artificial neural networks based on different architectures were trained and their results were compared.
Úplný záznam
Microstructural classification of multiphase steels by advanced microscopy and image analysis techniques
 Jozefovič, Patrik ; Materna, Jiří (oponent) ; Mikmeková, Šárka (vedoucí práce)
Austenitic stainless steels have found application in various sectors due to their characteristic properties. Metastable character of some of them, which allows martensitic transformation, is connected with possible risks in form of decrease of toughness. For revelation of martensitic phase in a microstructure of the austenitic stainless steel, techniques such as electron backscatter diffraction (EBSD) in scanning electron microscope (SEM) are used. However, the EBSD is time-consuming and it requires high quality of metallographic specimen preparation. Goal of this thesis is to find other techniques, which allow separation of the phases in metastable austenitic stainless steel in the SEM, as well as optimization of metallographic specimen preparation for needs of the SEM. After fulfilment of these goals, the thesis focuses on possibility of usage of the so-called deep learning for purpose of automated separation of phases in micrographs from the SEM. For this purpose, 4 artificial neural networks based on different architectures were trained and their results were compared.
Úplný záznam

Viz též: podobná jména autorů
5 MATERNA, Jan
5 Materna, Jan
Chcete být upozorněni, pokud se objeví nové záznamy odpovídající tomuto dotazu?
Přihlásit se k odběru RSS.
Digitální repozitář :: Hledej :: Nový záznam :: Personalizace :: Nápověda
Powered by Invenio v1.1.2
Spravuje admin.invenio@techlib.cz
Tato stránka je dostupná také v následujících jazycích:
Česky  English