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.

回收铝合金中的富铁金属间相通常表现出复杂的 3D 回旋结构和形态。它们是对回收铝合金的机械性能有害的常见金属间化合物相。在本研究中，我们使用同步加速器 X 射线断层扫描来研究铸态 Al-5Cu-1.5Fe-1Si 合金中富铁相、Al ₂ Cu 相和铸造缺陷的真实 3D 形貌。基于机器学习的图像处理方法用于识别和分割 3D 断层扫描图像堆栈中的不同阶段。在研究条件下，发现β-Al ₉ Fe ₂ Si ₂和ω-Al ₇ Cu ₂ Fe 是主要的富铁金属间相。β-Al ₉Fe ₂ Si ₂相表现出空间连接的3D 网络结构和形态，进而控制Al ₂ Cu 相和缩孔的3D 空间分布。凝固早期形成的Al ₃ Fe 相在很大程度上影响随后形成的富铁金属间相的结构和形貌。机器学习方法已被证明是在基于多维成像的材料表征工作中处理大数据集的强大工具。