About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
|
Symposium
|
Artificial Intelligence Applications in Integrated Computational Materials Engineering
|
Presentation Title |
Pushing the Limits of Fine Feature Detection in Deep-Learning Assisted 3D X-Ray Microscopy: Characterization of Hierarchical Microstructures in TiC Reinforced Nickel Matrix Composites |
Author(s) |
Kaushik Yanamandra, Hrishikesh Bale, Nathan Johnson, Raj Banerjee |
On-Site Speaker (Planned) |
Kaushik Yanamandra |
Abstract Scope |
Metal Matrix Composites (MMCs) have gained importance in materials science and engineering due to their remarkable properties and diverse applications. Ni-Ti-C based MMC, produced using the laser engineered net shaping process, exhibit unique hierarchical microstructures. These microstructures consist of an in-situ formed and uniformly distributed titanium carbide phase that reinforces the nickel matrix. The intricate microstructures span across various length scales, with different phases playing a critical role in determining the exceptional properties of Ni-Ti-C based MMCs. To gain insight into these, high-resolution and non-destructive X-ray microscopy (XRM) is leveraged. XRM provides insights into the spatial distribution and alignment of reinforcing phases by analyzing MMCs at sub-micron level, and at different length scales in 3D, within the metal matrix. A unique combination of specially designed high-resolution objective combined with deep learning-based 3D reconstruction are utilized to produce sub-micron resolutions for dense MMC samples. Overall, XRM contributes significantly to understanding MMCs. |
Proceedings Inclusion? |
Planned: |
Keywords |
Characterization, Machine Learning, |