| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
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Integration between Modeling and Experiments for Crystalline Metals: From Atomistic to Macroscopic Scales IV
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| Presentation Title |
Tailoring the Properties of Multi-phase Titanium Through the Use of Correlative Microscopy and Machine Learning |
| Author(s) |
Gunnar B. Blaschke, David P. Field , Colin Merriman |
| On-Site Speaker (Planned) |
Gunnar B. Blaschke |
| Abstract Scope |
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 (ML) techniques are 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 optical microscopy, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and electron backscatter diffraction (EBSD). Microstructural features such as α platelet dimensions and locations and α/β phase boundaries are analyzed and correlated with strength and fracture toughness. Conclusion of this project resulted in a Convolutional Neural Network (CNN) with the ability to segment and classify the microstructures of Ti 10V-2Fe-3Al. |