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: Focused ion beams
- All Subjects: stochastic reconstruction
- Creators: Chawla, Nikhilesh
Description
Nanolaminate composite materials consist of alternating layers of materials at the nanoscale (≤100 nm). Due to the nanometer scale thickness of their layers, these materials display unique and tailorable properties. This enables us to alter both mechanical attributes such as strength and wear properties, as well as functional characteristics such as biocompatibility, optical, and electronic properties. This dissertation focuses on understanding the mechanical behavior of the Al-SiC system. From a practical perspective, these materials exhibit a combination of high toughness and strength which is attractive for many applications. Scientifically, these materials are interesting due to the large elastic modulus mismatch between the layers. This, paired with the small layer thickness, allows a unique opportunity for scientists to study the plastic deformation of metals under extreme amounts of constraint.
Previous studies are limited in scope and a more diverse range of mechanical characterization is required to understand both the advantages and limitations of these materials. One of the major challenges with testing these materials is that they are only able to be made in thicknesses on the order of micrometers so the testing methods are limited to small volume techniques. This work makes use of both microscale testing techniques from the literature as well as novel methodologies. Using these techniques we are able to gain insight into aspects of the material’s mechanical behavior such as the effects of layer orientation, flaw dependent fracture, tension-compression asymmetry, fracture toughness as a function of layer thickness, and shear behavior as a function of layer thickness.
Previous studies are limited in scope and a more diverse range of mechanical characterization is required to understand both the advantages and limitations of these materials. One of the major challenges with testing these materials is that they are only able to be made in thicknesses on the order of micrometers so the testing methods are limited to small volume techniques. This work makes use of both microscale testing techniques from the literature as well as novel methodologies. Using these techniques we are able to gain insight into aspects of the material’s mechanical behavior such as the effects of layer orientation, flaw dependent fracture, tension-compression asymmetry, fracture toughness as a function of layer thickness, and shear behavior as a function of layer thickness.
ContributorsMayer, Carl Randolph (Author) / Chawla, Nikhilesh (Thesis advisor) / Jiang, Hanqing (Committee member) / Molina-Aldareguia, Jon (Committee member) / Rajagopalan, Jagannathan (Committee member) / Arizona State University (Publisher)
Created2016
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