Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Perreault, Francois
- Creators: Turaga, Pavan
Description
Glasses have many applications such as containers, substrates of displays, high strength fibers and portable electronic display panels. Their excellent mechanical properties such as high hardness, good forming ability and scratch resistance make glasses ideal for these applications. Many factors affect the selection of one glass over another for a given purpose such as cost, ingredients, scalability of manufacturing, etc. Typically, silicate based glasses are often selected because they satisfy most of the selection criteria. However, with the recent abundant use of these glasses in touch-based applications, understanding their abilities to dissipate energy due to surface contact loads has become increasingly desirable. The most common silicate glasses worldwide are glassy silica and soda lime. Calcium aluminosilicates are also gaining popularity due to their importance as substrates for display screens in electronic devices. The surface energy dissipation and strength of these glasses are based on several factors, but predominantly rely on ingredient composition and the so-called Indentation Size Effect (ISE), where the strength depends on the maximum surface force. Both the composition and ISE alter the strength and favored energy dissipation mechanisms of the glass. Unlocking the contribution of these mechanisms and elucidating their dependence on composition and force is the underlining goal of this thesis.Prior to cracking, silicate glasses can inelastically deform by shear and densification. However, the link between the mechanical properties, strength, glass structure and maximum force and the propensity by which either of these mechanisms are favored still remains unclear. In this study, the first aim is to elucidate the causes of the ISE and
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explore the relationships between the ISE and the dissipation mechanisms, and identify what feature(s) of the glass can be used to infer their behavior. All glasses have shown a strong link between the ISE and shear flow and densification. Second, the link between composition and the dissipation mechanisms will be elucidated. This is accomplished by performing indentation tests coupled with an annealing method to independently quantify the amount of volume associated with each dissipation mechanism and elucidate relationships with ingredients and structure of the glasses. Some conclusions will then be presented that link all these behaviors together.
ContributorsKazembeyki, Maryam (Author) / Hoover, Christian G (Thesis advisor) / Rajan, Subramaniam (Committee member) / Neithalath, Narayanan (Committee member) / Chawla, Nikhilesh (Committee member) / Perreault, Francois (Committee member) / Arizona State University (Publisher)
Created2021
Description
Semantic image segmentation has been a key topic in applications involving image processing and computer vision. Owing to the success and continuous research in the field of deep learning, there have been plenty of deep learning-based segmentation architectures that have been designed for various tasks. In this thesis, deep-learning architectures for a specific application in material science; namely the segmentation process for the non-destructive study of the microstructure of Aluminum Alloy AA 7075 have been developed. This process requires the use of various imaging tools and methodologies to obtain the ground-truth information. The image dataset obtained using Transmission X-ray microscopy (TXM) consists of raw 2D image specimens captured from the projections at every beam scan. The segmented 2D ground-truth images are obtained by applying reconstruction and filtering algorithms before using a scientific visualization tool for segmentation. These images represent the corrosive behavior caused by the precipitates and inclusions particles on the Aluminum AA 7075 alloy. The study of the tools that work best for X-ray microscopy-based imaging is still in its early stages.
In this thesis, the underlying concepts behind Convolutional Neural Networks (CNNs) and state-of-the-art Semantic Segmentation architectures have been discussed in detail. The data generation and pre-processing process applied to the AA 7075 Data have also been described, along with the experimentation methodologies performed on the baseline and four other state-of-the-art Segmentation architectures that predict the segmented boundaries from the raw 2D images. A performance analysis based on various factors to decide the best techniques and tools to apply Semantic image segmentation for X-ray microscopy-based imaging was also conducted.
In this thesis, the underlying concepts behind Convolutional Neural Networks (CNNs) and state-of-the-art Semantic Segmentation architectures have been discussed in detail. The data generation and pre-processing process applied to the AA 7075 Data have also been described, along with the experimentation methodologies performed on the baseline and four other state-of-the-art Segmentation architectures that predict the segmented boundaries from the raw 2D images. A performance analysis based on various factors to decide the best techniques and tools to apply Semantic image segmentation for X-ray microscopy-based imaging was also conducted.
ContributorsBarboza, Daniel (Author) / Turaga, Pavan (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2020