Characterization of the Convoluted 3D Intermetallic Phases in a Recycled Al Alloy by Synchrotron X-ray Tomography and Machine Learning

Abstract

Fe-rich intermetallic phases in recycled Al alloys often exhibit complex and 3D convoluted structures and morphologies. They are the common detrimental intermetallic phases to the mechanical properties of recycled Al alloys. In this study, we used synchrotron X-ray tomography to study the true 3D morphologies of the Fe-rich phases, Al2Cu phases and casting defects in an as-cast Al-5Cu-1.5Fe-1Si 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-Al7Cu2Fe 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 affect to a large extent 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.