Microstructural characterization of QC-forming Al-Mn-based alloy using machine learning softwareZaky, Adam (Avtor) Leskovar, Blaž (Avtor) Naglič, Iztok (Avtor) Markoli, Boštjan (Avtor) aluminum alloysquasicrystalsnucleationmachine learningcharacterizationThe main objective was to investigate and evaluate the influence of TiC and TiB$_2$ inoculants on the formation of not only the icosahedral quasicrystalline phase (IQC) but also the β-phase in our Al-Mn-Si-Cu-Mg alloy. First, the presence of both phases was confirmed using electron backscatter diffraction (EBSD), followed by microstructural segmentation and quantification using the open-source machine learning software ilastik and Fiji. The ilastik software was selected because it allowed us to use different parameters to distinguish between the IQC and β-AlMnSi phases, which otherwise have similar color/Z contrast and are difficult to distinguish in a timely manner using other methods. The analyses were performed on a total of 3662 images taken during optical light microscopy. The results show that TiC inoculants better promote the ability to form IQC compared to TiB$_2$. The use of TiC resulted in an increase of 40% compared to only 14% when TiB$_2$ was used. Exceeding the TiC threshold of 0.0224 wt.% resulted in a 571% increase in the amount of β-phase compared to our non-inoculated alloy. Microhardness measurements were carried out on the IQC phase using the Vickers method, and an average value of 680 HV0.01 was obtained.20252025-03-10 12:12:54Članek v reviji167743sl