ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- All Subjects: stochastic reconstruction
- All Subjects: Thin metal films
- Creators: Chawla, Nikhilesh
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
An accurate knowledge of the complex microstructure of a heterogeneous material is crucial for quantitative structure-property relations establishment and its performance prediction and optimization. X-ray tomography has provided a non-destructive means for microstructure characterization in both 3D and 4D (i.e., structural evolution over time). Traditional reconstruction algorithms like filtered-back-projection (FBP) method or algebraic reconstruction techniques (ART) require huge number of tomographic projections and segmentation process before conducting microstructural quantification. This can be quite time consuming and computationally intensive.
In this thesis, a novel procedure is first presented that allows one to directly extract key structural information in forms of spatial correlation functions from limited x-ray tomography data. The key component of the procedure is the computation of a “probability map”, which provides the probability of an arbitrary point in the material system belonging to specific phase. The correlation functions of interest are then readily computed from the probability map. Using effective medium theory, accurate predictions of physical properties (e.g., elastic moduli) can be obtained.
Secondly, a stochastic optimization procedure that enables one to accurately reconstruct material microstructure from a small number of x-ray tomographic projections (e.g., 20 - 40) is presented. Moreover, a stochastic procedure for multi-modal data fusion is proposed, where both X-ray projections and correlation functions computed from limited 2D optical images are fused to accurately reconstruct complex heterogeneous materials in 3D. This multi-modal reconstruction algorithm is proved to be able to integrate the complementary data to perform an excellent optimization procedure, which indicates its high efficiency in using limited structural information.
Finally, the accuracy of the stochastic reconstruction procedure using limited X-ray projection data is ascertained by analyzing the microstructural degeneracy and the roughness of energy landscape associated with different number of projections. Ground-state degeneracy of a microstructure is found to decrease with increasing number of projections, which indicates a higher probability that the reconstructed configurations match the actual microstructure. The roughness of energy landscape can also provide information about the complexity and convergence behavior of the reconstruction for given microstructures and projection number.
In this thesis, a novel procedure is first presented that allows one to directly extract key structural information in forms of spatial correlation functions from limited x-ray tomography data. The key component of the procedure is the computation of a “probability map”, which provides the probability of an arbitrary point in the material system belonging to specific phase. The correlation functions of interest are then readily computed from the probability map. Using effective medium theory, accurate predictions of physical properties (e.g., elastic moduli) can be obtained.
Secondly, a stochastic optimization procedure that enables one to accurately reconstruct material microstructure from a small number of x-ray tomographic projections (e.g., 20 - 40) is presented. Moreover, a stochastic procedure for multi-modal data fusion is proposed, where both X-ray projections and correlation functions computed from limited 2D optical images are fused to accurately reconstruct complex heterogeneous materials in 3D. This multi-modal reconstruction algorithm is proved to be able to integrate the complementary data to perform an excellent optimization procedure, which indicates its high efficiency in using limited structural information.
Finally, the accuracy of the stochastic reconstruction procedure using limited X-ray projection data is ascertained by analyzing the microstructural degeneracy and the roughness of energy landscape associated with different number of projections. Ground-state degeneracy of a microstructure is found to decrease with increasing number of projections, which indicates a higher probability that the reconstructed configurations match the actual microstructure. The roughness of energy landscape can also provide information about the complexity and convergence behavior of the reconstruction for given microstructures and projection number.
ContributorsLi, Hechao (Author) / Jiao, Yang (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Liu, Yongming (Committee member) / Ren, Yi (Committee member) / Mu, Bin (Committee member) / Arizona State University (Publisher)
Created2017