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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.
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Automation of metallographic sample cleaning process
Čermák, Jan ; Ambrož, Ondřej ; Jozefovič, Patrik ; Mikmeková, Šárka
Specimen cleaning and drying are critical processes following any metallographic preparation steps. The paper focuses on automation by reason of absence of the process repeatability during manual sample handling. An etchant or electrolyte results in inhomogeneous surface quality because the solution runs off the specimen surface during its removal from the beaker. High-quality specimen cleaning is absolutely crucial for the acquisition of the specimen suitable for characterization by a scanning electron microscope operated at very low landing energies of the primary electrons (SLEEM). The SLEEM technique is a powerful tool for the characterization of advanced steels, as described by many scientific papers. The SLEEM requires the specimen absolutely free of water and any organic residues on the surface. This work presents a novel unique apparatus enabling automatic specimen cleaning and drying after the etching or electropolishing processes. Automation reduces the influence of dependent variables that would be introduced into the process by the metallographer. These variables include cleaning time, kinematics, and motion dynamics, but the process can also be affected by variables that are not obvious. Performed experiments clearly demonstrate our in-house designed apparatus as a useful tool improving efficiency and consistency of the sample cleaning process. The high quality of the specimen surface is verified using a light optical microscope, an electron scanning microscope, and above mentioned SLEEM technique.
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Artifacts and errors in EBSD mapping of retained austenite in TRIP steel
Mikmeková, Šárka ; Jozefovič, Patrik ; Ambrož, Ondřej
The present work aims to demonstrate artifacts and errors in visualization of retained austenite phase in TRIP steel by an electron back-scattered diffraction (EBSD) technique. Retained austenite phases size and shape obtained by the EBSD are directly compared with a real image of these phases acquired by means of an atomic force microscopy (AFM). The effect of the step size parameter used for the EBSD analysis on the retained austenite phase fraction and morphology is discussed in detail and quantified. Surface roughness as a barrier for the imaging of fine features situated on a specimen surface is demonstrated.
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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.
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