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: Lefky, Christopher
- Creators: Turaga, Pavan
Description
Additive manufacturing (AM) describes an array of methods used to create a 3D object layer by layer. The increasing popularity of AM in the past decade has been due to its demonstrated potential to increase design flexibility, produce rapid prototypes, and decrease material waste. Temporary supports are an inconvenient necessity in many metal AM parts. These sacrificial structures are used to fabricate large overhangs, anchor the part to the build substrate, and provide a heat pathway to avoid warping. Polymers AM has addressed this issue by using support material that is soluble in an electrolyte that the base material is not. In contrast, metals AM has traditionally approached support removal using time consuming, costly methods such as electrical discharge machining or a dremel.
This work introduces dissolvable supports to single- and multi-material metals AM. The multi-material approach uses material choice to design a functionally graded material where corrosion is the functionality being varied. The single-material approach is the primary focus of this thesis, leveraging already common post-print heat treatments to locally alter the microstructure near the surface. By including a sensitizing agent in the ageing heat treatment, carbon is diffused into the part decreasing the corrosion resistance to a depth equal to at least half the support thickness. In a properly chosen electrolyte, this layer is easily chemically, or electrochemically removed. Stainless steel 316 (SS316) and Inconel 718 are both investigated to study this process using two popular alloys. The microstructure evolution and corrosion properties are investigated for both. For SS316, the effect of applied electrochemical potential is investigated to describe the varying corrosion phenomena induced, and the effect of potential choice on resultant roughness. In summary, a new approach to remove supports from metal AM parts is introduced to decrease costs and further the field of metals AM by expanding the design space.
This work introduces dissolvable supports to single- and multi-material metals AM. The multi-material approach uses material choice to design a functionally graded material where corrosion is the functionality being varied. The single-material approach is the primary focus of this thesis, leveraging already common post-print heat treatments to locally alter the microstructure near the surface. By including a sensitizing agent in the ageing heat treatment, carbon is diffused into the part decreasing the corrosion resistance to a depth equal to at least half the support thickness. In a properly chosen electrolyte, this layer is easily chemically, or electrochemically removed. Stainless steel 316 (SS316) and Inconel 718 are both investigated to study this process using two popular alloys. The microstructure evolution and corrosion properties are investigated for both. For SS316, the effect of applied electrochemical potential is investigated to describe the varying corrosion phenomena induced, and the effect of potential choice on resultant roughness. In summary, a new approach to remove supports from metal AM parts is introduced to decrease costs and further the field of metals AM by expanding the design space.
ContributorsLefky, Christopher (Author) / Hildreth, Owen (Thesis advisor) / Chawla, Nikhilesh (Committee member) / Azeredo, Bruno (Committee member) / Rykaczewski, Konrad (Committee member) / Nian, Qiong (Committee member) / Arizona State University (Publisher)
Created2018
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