About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Thermodynamics and Kinetics
|
Presentation Title |
Investigating Temperature-Dependent Crack Susceptibility in Nickel-Based Superalloys Using Thermo-Calc and Machine Learning |
Author(s) |
mohammad younes araghi, Shuozhi Xu |
On-Site Speaker (Planned) |
mohammad younes araghi |
Abstract Scope |
In this study, we aim to identify the correlation between the crack susceptibility coefficient (CSC), a prevalent challenge in the additive manufacturing of superalloys, and temperature using Thermo-Calc software. The CSC is critical in determining the structural integrity and performance of superalloys during and after the manufacturing process. By simulating various temperature conditions, we will analyze the impact on CSC values, thereby establishing a detailed understanding of the relationship between temperature variations and crack susceptibility. Subsequently, we will leverage this data to develop a machine learning (ML) model for predictive feature analysis. Specifically, the model will evaluate how the addition of different elements to nickel-based superalloys influences the CSC. This dual approach, combining thermodynamic simulation with ML, aims to enhance the predictability and optimization of alloy compositions, ultimately contributing to more robust and reliable additive manufacturing processes for superalloys. |
Proceedings Inclusion? |
Planned: |
Keywords |
Modeling and Simulation, Machine Learning, Computational Materials Science & Engineering |