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: Niauzorau, Stanislau
- Creators: Turaga, Pavan
Description
In recent years, the scientific community around the synthesis and processing of nanoporous metals is striving to integrate them into powder metallurgy processes such as additive manufacturing since it has a potential to fabricate 3D hierarchical high surface area electrodes for energy applications. Recent research in dealloying – a versatile method for synthesizing nanoporous metals – emphasized the need in understanding its process-structure relationships to independently control the relative density, ligament and pore sizes with good process reproducibly. In this dissertation, a new understanding of the dealloying process is presented for synthesizing (i) nanoporous gold thin-films and (ii) nanoporous Cu spherical powders with an emphasis on understanding variability in their process-structure relationships and process scalability. First, this work sheds the light on the nature of the dealloying front and its percolation along the grain boundaries in nanocrystalline gold-silver thin films by studying the early stages of ligament nucleation. Additionally, this work analyses its variability by investigating new process variables such as (i) equilibration time and (ii) precursor aging and their impacts in achieving process reproducibility. The correlation of relative density with ligament size is contextualized with state-of-the-art data mining research. Second, this work provides a new methodology for large scale production of nanoporous Cu powder and demonstrates its integration with powder casting to fabricate porous conductive electrode. By understanding the influence of etching solution concentration and titration methodology on the structure and composition of nanoporous Cu, it was possible to fabricate precipitate-free powders at high throughputs. Further, the nature of oxygen incorporation into porous Cu powder was studied as a function of surface-to-volume ratio of powder in atmospheric conditions. To consolidate powders into parts via open-die casting, this work harvests Ostwald Ripening phenomena associated with thermal coarsening in nanoporous metals to weld them at low temperatures (approximately one-third of its melting temperature). This work represents a major step towards the integration of nanoporous Cu feedstocks into additive manufacturing.
ContributorsNiauzorau, Stanislau (Author) / Azeredo, Bruno (Thesis advisor) / Sieradzki, Karl (Committee member) / Song, Kenan (Committee member) / Chawla, Nikhilesh (Committee member) / Arizona State University (Publisher)
Created2022
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