About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Aluminum Alloys, Processing and Characterization
|
Presentation Title |
Machine Learning Enhanced Synchrotron X-ray Tomography Analysis of the Convoluted 3D Fe-rich Intermetallic Phases in a Recycled Al Alloy |
Author(s) |
Zhiguo Zhang, Ling Qin, Jiawei Mi |
On-Site Speaker (Planned) |
Zhiguo Zhang |
Abstract Scope |
Fe-rich intermetallic phases in Al alloys often exhibit complex and 3D convoluted structures and morphologies. In this study, we used synchrotron X-ray tomography to study the true 3D morphologies of the Fe-rich phases in an as-cast recycled Al alloy. Machine learning based image processing approach was used to recognize and segment the different phases in the 3D tomography image stacks. In the studied condition, the β-Al9Fe2Si2 and ω-Al7Cu2 are found to be the main Fe-rich intermetallic phases. The β-Al9Fe2Si2 phases exhibit a spatially connected 3D network structure and morphology which in turn control the 3D spatial distribution of the Al2Cu phases and the shrinkage cavities. The Al3Fe phases formed at the early stage of solidification affects the structure and morphology of the subsequently formed Fe-rich intermetallic phases. The machine learning method has been demonstrated as a powerful tool for processing big datasets in multidimensional imaging-based materials characterization work |
Proceedings Inclusion? |
Planned: Light Metals |
Keywords |
Aluminum, Machine Learning, Solidification |