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: Izadi, Ehsan
- Creators: Turaga, Pavan
Description
Nanocrystalline (NC) and Ultrafine-grained (UFG) metal films exhibit a wide range of enhanced mechanical properties compared to their coarse-grained counterparts. These properties, such as very high strength, primarily arise from the change in the underlying deformation mechanisms. Experimental and simulation studies have shown that because of the small grain size, conventional dislocation plasticity is curtailed in these materials and grain boundary mediated mechanisms become more important. Although the deformation behavior and the underlying mechanisms in these materials have been investigated in depth, relatively little attention has been focused on the inhomogeneous nature of their microstructure (particularly originating from the texture of the film) and its influence on their macroscopic response. Furthermore, the rate dependency of mechanical response in NC/UFG metal films with different textures has not been systematically investigated. The objectives of this dissertation are two-fold.
The first objective is to carry out a systematic investigation of the mechanical behavior of NC/UFG thin films with different textures under different loading rates. This includes a novel approach to study the effect of texture-induced plastic anisotropy on mechanical behavior of the films. Efforts are made to correlate the behavior of UFG metal films and the underlying deformation mechanisms. The second objective is to understand the deformation mechanisms of UFG aluminum films using in-situ transmission electron microscopy (TEM) experiments with Automated Crystal Orientation Mapping. This technique enables us to investigate grain rotations in UFG Al films and to monitor the microstructural changes in these films during deformation, thereby revealing detailed information about the deformation mechanisms prevalent in UFG metal films.
The first objective is to carry out a systematic investigation of the mechanical behavior of NC/UFG thin films with different textures under different loading rates. This includes a novel approach to study the effect of texture-induced plastic anisotropy on mechanical behavior of the films. Efforts are made to correlate the behavior of UFG metal films and the underlying deformation mechanisms. The second objective is to understand the deformation mechanisms of UFG aluminum films using in-situ transmission electron microscopy (TEM) experiments with Automated Crystal Orientation Mapping. This technique enables us to investigate grain rotations in UFG Al films and to monitor the microstructural changes in these films during deformation, thereby revealing detailed information about the deformation mechanisms prevalent in UFG metal films.
ContributorsIzadi, Ehsan (Author) / Rajagopalan, Jagannathan (Thesis advisor) / Peralta, Pedro (Committee member) / Chawla, Nikhilesh (Committee member) / Solanki, Kiran (Committee member) / Oswald, Jay (Committee member) / Arizona State University (Publisher)
Created2017
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