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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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