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: Dai, Lenore
- Creators: Turaga, Pavan
Description
Gels are three-dimensional polymer networks with entrapped solvent (water etc.). They bear amazing features such as stimuli-responsive (temperature, PH, electric field etc.), high water content and biocompatibility and thus find a lot of applications. To understand the complex physics behind gel's swelling phenomenon, it is important to build up fundamental mechanical model and extend to complicated cases. In this dissertation, a coupled large deformation and diffusion model regarding gel's swelling behavior is presented. In this model, free-energy of the total gel is constituted by polymer stretching energy and polymer-solvent mixing energy. In-house nonlinear finite element code is implemented with fast computational capability. Complex phenomenon such as buckling and healing of cracked gel by swelling are studied. Due to the wide coverage of polymeric materials and solvents, solvent diffusion in gels not only follows Fickian diffusion law where concentration map is continuous but also follows non-Fickian diffusion law where concentration map shows high gradient. Phenomenological model with viscoelastic polymer constitutive and concentration dependent diffusivity is created. The model well captures this special diffusion phenomenon such as sharp diffusion front and distinctive swollen and unswollen region.
ContributorsZhang, Jiaping (Author) / Jiang, Hanqing (Thesis advisor) / Peralta, Pedro (Committee member) / Dai, Lenore (Committee member) / Rajan, Subramaniam D. (Committee member) / Chawla, Nikhilesh (Committee member) / Arizona State University (Publisher)
Created2012
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