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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Modeling 3D-Printed Composite Honeycomb Structures with the Representative Lattice Element Method

Description
The goal of our research was to develop and validate a method for predicting the mechanical behavior of Additively Manufactured multi-material honeycomb structures. Multiple approaches already exist in the field for modeling the behavior of cellular materials, including the bulk property assumption, homogenization and strut level characterization [1]. With the…

The goal of our research was to develop and validate a method for predicting the mechanical behavior of Additively Manufactured multi-material honeycomb structures. Multiple approaches already exist in the field for modeling the behavior of cellular materials, including the bulk property assumption, homogenization and strut level characterization [1]. With the bulk property approach, the structure is assumed to behave according to what is known about the material in its bulk formulation, without regard to its geometry or scale. With the homogenization technique, the specimen that is being tested is treated as a solid material within the simulation environment even if the physical specimen is not. Then, reduced mechanical properties are assigned to the specimen to account for any voids that exist within the physical specimen. This approach to mechanical behavior prediction in cellular materials is shape dependent. In other words, the same model cannot be used from one specimen to the next if the cell shapes of those lattices differ in any way. When using the strut level characterization approach, a single strut (the connecting member between nodes constituting a cellular material) is isolated and tested. With this approach, there tends to be a significant deviation in the experimental data due to the small size of the isolated struts. Yet it has the advantage of not being shape sensitive, at least in principle. The method that we developed, and chose to test lies within the latter category, and is what we have coined as the Representative Lattice Element (RLE) Method. This method is modeled after the well-established Representative Volume Element (RVE) method [2]. We define the RLE as the smallest unit over which mechanical tests can be conducted that will provide results which are representative of the larger lattice structure. In other words, the theory is that a single member (or beam in this case) of a honeycomb structure can be taken, tests can be conducted on this member to determine the mechanical properties of the representative lattice element and the results will be representative of the mechanical behavior whole structure. To investigate this theory, we designed specimens, conducted various tensile and compression tests, analyzed the recorded data, conducted a micromechanics study, and performed structural simulation work using commercial Finite Element Analysis software.
ContributorsSalti, Ziyad Zuheir (Co-author) / Eppley, Trevor (Co-author) / Bhate, Dhruv (Thesis director) / Song, Kenan (Committee member) / Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Capillary Action in Additive Manufactured Channels for Thermal Management

Description

Due to the vast increase in processing power and energy usage in computing, a need for greater heat dissipation is prevalent. With numerous applications demanding cheaper and more efficient options for thermal management, new technology must be employed. Through the use of additive manufacturing, designs and structures can be created…

Due to the vast increase in processing power and energy usage in computing, a need for greater heat dissipation is prevalent. With numerous applications demanding cheaper and more efficient options for thermal management, new technology must be employed. Through the use of additive manufacturing, designs and structures can be created that were not physically possible before without extensive costs. The goal is to design a system that utilizes capillary action, which is the ability for liquids to flow through narrow spaces unassisted. The level of detail required may be achieved with direct metal laser sintering (DMLS) and stereolithography (SLA) 3D printing.
ContributorsFechter, Andrew (Author) / Bhate, Dhruv (Thesis director) / Frank, Daniel (Committee member) / Engineering Programs (Contributor, 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