Barrett

Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.

Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.

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Print, Lock, and Roll: Design of a Parametric, Print-in-Place, Self-Locking Hinge

Description

While many 3D printed structures are rigid and stationary, the potential for complex geometries offers a chance for creative and useful motion. Printing structures larger than the print bed, reducing the need for support materials, maintaining multiple states without actuation, and mimicking origami folding are some of the opportunities offered…

While many 3D printed structures are rigid and stationary, the potential for complex geometries offers a chance for creative and useful motion. Printing structures larger than the print bed, reducing the need for support materials, maintaining multiple states without actuation, and mimicking origami folding are some of the opportunities offered by 3D printed hinges. Current efforts frequently employ advanced materials and equipment that are not available to all users. The purpose of this project was to develop a parametric, print-in-place, self-locking hinge that could be printed using very basic materials and equipment. Six main designs were developed, printed, and tested for their strength in maintaining a locked position. Two general design types were used: 1) sliding hinges and 2) removable pin hinges. The test results were analyzed to identify and explain the causes of observed trends. The amount of interference between the pin vertex and knuckle hole edge was identified as the main factor in hinge strength. After initial testing, the designs were modified and applied to several structures, with successful results for a collapsible hexagon and a folding table. While the initial goal was to have one CAD model as a final product, the need to evaluate tradeoffs depending on the exact application made this impossible. Instead, a set of design guidelines was created to help users make strategic decisions and create their own design. Future work could explore additional scaling effects, printing factors, or other design types.
ContributorsAndreotti, Jaimee Jeannette (Author) / Bhate, Dhruv (Thesis director) / Aukes, Daniel (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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Surface Pattern Recognition for Image-Based Inference of Mechanical Properties

Description
A key aspect of understanding the behavior of materials and structures is the analysis of how they fail. A key aspect of failure analysis is the discipline of fractography, which identifies features of interest on fracture surfaces with the goal of revealing insights on the nature of defects and microstructure,…

A key aspect of understanding the behavior of materials and structures is the analysis of how they fail. A key aspect of failure analysis is the discipline of fractography, which identifies features of interest on fracture surfaces with the goal of revealing insights on the nature of defects and microstructure, and their interactions with the environment such as loading conditions. While fractography itself is a decades-old science, two aspects drive the need for this research: (i) Fractography remains a specialized domain of materials science where human subjectivity and experience play a large role in accurate determination of fracture modes and their relationship to the loading environment. (ii) Secondly, Additive Manufacturing (AM) is increasingly being used to create critical functional parts, where our understanding of failure mechanisms and how they relate to process and post-process conditions is nascent. Given these two challenges, this thesis conducted work to train convolutional neural network (CNN) models to analyze fracture surfaces in place of human experts and applies this to Inconel 718 specimens fabricated with the Laser Powder Bed Fusion (LPBF) process, as well as to traditional sheet metal specimens of the same alloy. This work intends to expand on previous work utilizing clustering methods through comparison of models developed using both manufacturing processes to demonstrate the effectiveness of the CNN approach, as well as elucidate insights into the nature of fracture modes in additively and traditionally manufactured thin-wall Inconel 718 specimens.
ContributorsVan Handel, Nicole (Author) / Bhate, Dhruv (Thesis director, Committee member) / Guo, Shenghan (Thesis director, Committee member) / Barrett, The Honors College (Contributor) / Engineering Programs (Contributor) / Dean, W.P. Carey School of Business (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05