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In this study, microstructural evolution during solidification of a hypereutectic Al–Mn–Fe–Si alloy was investigated using semi-quantitative two-/three-dimensional phase-field modeling. The formation of facetted Al6Mn precipitates and the temperature evolution during solidification were simulated and experimentally validated. The temperature evolution obtained from the phase-field simulation, which was balanced between extracted heat and latent heat release, was compared to the thermal profile of the specimen measured during casting to validate the semi-quantitative phase-field simulation. The casting microstructure, grain morphology, and solute distribution of the specimen were analyzed using electron backscatter diffraction and energy-dispersive spectroscopy and compared with the simulated microstructure. The simulation results identified the different Fe to Mn ratios in Al6(Mnx,Fe1−x) precipitates that formed during different solidification stages and were confirmed by energy-dispersive spectroscopy. The precipitates formed in the late solidification stage were more enriched with Fe than the primary precipitate due to solute segregation in the interdendritic channel. The semi-quantitative model facilitated a direct comparison between the simulation and experimental observations.

The copper molten marks at a fire site provide important clues for determining the causes of fire. Four factors have been presented to quantitatively discriminate copper molten marks, namely the fraction of (001) component perpendicular to the demarcation line, the grain aspect ratio, the fraction of Σ3 boundaries, and the fraction of maximum grain size. However, only laboratory-level results of these parameters have been presented, and their applicability in actual fires is yet to be verified. In this study, a fire reproduction experimental system was configured to generate molten marks similar to those in actual fire sites. The molten marks were measured by electron backscatter diffraction and applied to the four discriminant factors. The results obtained similar characteristics to those of the laboratory unit, confirming the applicability of the four discriminant factors. Discriminant equations and processes that can distinguish the primary and secondary arc beads were derived using the molten marks generated in the laboratory and reproduction experiments. Furthermore, a probabilistic discrimination method and classification model developed by machine learning were proposed. Therefore, the use of the discriminants in actual fires can improve the reliability of the statistics and prevent the recurrence of similar fires.