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CC BY V4.0, Open Access
Thesis
Tailoring the Properties of Multi-Phase Titanium Through the Use of Imaging Modalit Y and Machine Learning
Washington State University
Master of Science (MS), Washington State University
2023
DOI:
https://doi.org/10.7273/000005265
Abstract
High strength alloys with good ductility, hardness, and toughness are needed to meet rigorous design requirements for extreme environments. However, metals rarely exhibit both high strength and good fracture toughness as the underlying mechanisms work in opposition. An exception can be found in multiphase alloys that form microstructures of mixed phases. Machine learning techniques can be used to identify and correlate critical microstructural features in a Ti-10V-2Fe-3Al alloy that is reported to exhibit high strength and fracture toughness. Metallurgical specimens are characterized in a correlative manor using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and electron backscatter diffraction (EBSD). Microstructural features are analyzed and will be used to construct a training dataset for a convolutional neural network (CNN) model to gain the ability to segment and classify the microstructures of Ti 10V-2Fe-3Al and relate microstructure to processing and properties.
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Details
- Title
- Tailoring the Properties of Multi-Phase Titanium Through the Use of Imaging Modalit Y and Machine Learning
- Creators
- Gunnar Benjamen Blaschke
- Contributors
- David P Field (Advisor)Colin C Merriman (Advisor)Narasimha Boddeti (Committee Member)Scott P Beckman (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Mechanical and Materials Engineering, School of
- Theses and Dissertations
- Master of Science (MS), Washington State University
- Publisher
- Washington State University
- Number of pages
- 104
- Identifiers
- 99901019235501842
- Language
- English
- Resource Type
- Thesis