About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data informatics: Design of Structural Materials
|
Presentation Title |
Discovery of Optimized ω-phase Free Ti-based Alloys Using CALPHAD and Artificial Intelligence Approach |
Author(s) |
George S. Dulikravich, Rajesh Jha |
On-Site Speaker (Planned) |
Rajesh Jha |
Abstract Scope |
Ti-Ta-Nb-Sn-Mo-Zr alloy system was divided into 26 sub-groups of 6, 5, 4 and 3 elements at a time. Each group was studied for various phase transformations using CALPHAD approach. Omega (ω) phase is detrimental as it severely impacts mechanical properties and causes embrittlement in shape memory alloys based on titanium. It is a metastable phase and it is formed during heat treatment. All 26 sub-groups were studied for determining phase stability of Omega (ω) phase from -242 ˚C to 1231 ˚C, and ω-phase was observed up to 876 ˚C. Chemistries of several sets of Omega (ω) phase free Ti-based alloys were found. For CALPHAD approach, we used Thermocalc software. We also used supervised machine learning and developed models using Deep Learning libraries (TensorFlow/Keras) in Python. We analyzed our data using unsupervised machine learning where we used Self-Organizing Maps (SOM) and Hierarchical Clustering Analysis (HCA) for discovering patterns within the dataset. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, ICME, Machine Learning |