About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advanced Characterization Techniques for Quantifying and Modeling Deformation
|
| Presentation Title |
Application of X-Ray Microscopy and Machine Learning in Analyzing Metal Matrix Syntactic Foams |
| Author(s) |
Kaushik Yanamandra, Noushin Moharrami, Nikhil Gupta |
| On-Site Speaker (Planned) |
Kaushik Yanamandra |
| Abstract Scope |
The integration of advanced microscopy techniques into industrial applications has revolutionized
the way materials are analyzed and developed. This session will focus on the use of X-ray
microscopy, particularly in the context of Metal Matrix Syntactic Foams (MMSFs), which are
increasingly utilized in various industries due to their lightweight and high-strength properties. X-ray microscopy offers unparalleled insights into the internal structure and composition of MMSFs,
enabling precise characterization of their microstructural features.
By leveraging X-ray microscopy, we can obtain high-resolution, three-dimensional images that
reveal the distribution of metal matrix and hollow spheres within the foam. This capability is
crucial for quality control and failure analysis, allowing manufacturers to identify defects and
optimize production processes. Furthermore, the data generated from X-ray microscopy can be
enhanced through machine learning algorithms - DeepRecon Pro, facilitating advanced data
analysis that supports metrology in manufacturing environments. In this work accurate materials performance model is developed. |
| Proceedings Inclusion? |
Planned: |