Publication Date
2025
Document Type
Thesis
Committee Members
Amir A. Farajian, Ph.D. (Advisor); Marina B. Ruggles-Wrenn, Ph.D. (Committee Member); Harok Bae, Ph.D. (Committee Member)
Degree Name
Master of Science in Mechanical Engineering (MSME)
Abstract
Ultra-high-temperature ceramics, commonly used in aerospace applications, operate in high-temperature oxidizing environments where a surface scale forms and regulates oxy gen access. For hafnium diboride–silicon carbide (HfB2–SiC), that scale consists of a borosilicate glass layer over a porous HfO2 skeleton. Oxygen transport through this poros ity governs the kinetics of mechanistic models, requiring reproducible inputs for accurate pore fraction (PF), pore-size distributions, and ultimately tortuosity. This thesis replaces rule-based SEM thresholding with a convolutional neural network that segments pores and predicts the pore-radius distribution for transport models. The resulting calibrated porosity maps and size distributions transfer within the acquisition domain and plug directly into mechanistic oxidation models.
Page Count
113
Department or Program
Department of Mechanical and Materials Engineering
Year Degree Awarded
2025
Copyright
Copyright 2025, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
ORCID ID
0009-0006-9196-0008
