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: Materials Science and Engineering Program
- Creators: Turaga, Pavan
Description
This dissertation investigates the potential for enzyme induced carbonate cementation as an alternative to Portland cement for creating building material from sand aggregate. We create a solution of urease enzyme, calcium chloride (CaCl2), and urea in water and added sand. The urease catalyzes the synthesis of carbonate from urea, and the carbonate then bonds with a dissociated calcium ion and precipitates from the solution as calcium carbonate (CaCO3). This precipitate can form small crystal bridges at contacts between sand grains that lock the sand grains in place. Using enzyme induced carbonate precipitation we created a cemented sand sample with a maximum compressive strength of 319 kPa and an elastic modulus of approximately 10 MPa. Images from the SEM showed that a major failure mechanism in the cemented samples was the delamination of the CaCO3 from the sand grains. We observed that CaCO3 cementation did not when solutions with high concentrations of CaCl2 and urea were used.
ContributorsBull, Michael Ryan (Author) / Kavazanjian, Edward (Thesis director) / Chawla, Nikhilesh (Committee member) / Barrett, The Honors College (Contributor) / Materials Science and Engineering Program (Contributor)
Created2014-05
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