عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
TL49630
انگلیسی
Studies on Neutron Diffraction and X-ray Radiography for Material Inspection
[Thesis]
Fahima Fahmida Islam
Lee, Hyoung K.
Missouri University of Science and Technology
2017
89
Committee members: Alajo, Ayodeji B.; Liu, Xin; Moss, Randy H.; Schlegel, Joshua P.
Place of publication: United States, Ann Arbor; ISBN=978-0-355-25471-6
Ph.D.
Nuclear Engineering
Missouri University of Science and Technology
2017
Among the different probes to study the structures of the bio and structural materials, X-ray and neutron are widely used because of their distinctive usefulness in investigating different structures. X-ray radiography and neutron diffraction are two widely known non-destructive techniques for material inspection. Here we demonstrate the design of neutron diffractometer with low power source and analyze the digital image produced by the X-ray radiography instead of neutron diffraction because of the availability of the data. Neutron diffraction is a powerful tool for understanding the behavior of crystal structures and phase behaviors of materials. While neutron diffraction capabilities continue to explore new frontiers of materials science, such capabilities currently exist in limited places, which require high neutron flux. The study seeks to design a low-resolution neutron diffraction system that can be installed on low power reactors (e.g. 250 kW thermal power). The performance of the diffractometer is estimated using Monte-Carlo ray-tracing simulations with McStas with an application in material science. Both monochromatic and polychromatic configurations are considered in order to maximize the net diffracted neutron flux at the detectors with reasonable resolution. On the other hand, considering X-ray radiography as a structure inspecting technique, analysis of dental X-ray panorama is performed for the detection of oral lesions. A novel automatic computer-aided method to identify dental lesions from dental X-ray is presented. Morphological operations, intensity profile analysis, automated seed point selection, region growing, feature extraction and neural network application are carried out to perform the job. Results show that the performance of the proposed method surpasses existing automated methods utilizing dental X-rays.
Engineering
Applied sciences;Diffraction;Inspection;Neutron;Radiography;Ray;X-ray
Evans, Michael P.
Lee, Hyoung K.
Nuclear Engineering
Missouri University of Science and Technology
1970011002; 10603370
مطالعه متن کتاب p
[Thesis]
276903
a
Y
