Abstract

For aluminum alloys, grain refinement is the most efficient method to improve both strength and ductility. However, this rule may not apply for the recycled Al due to the large amount of intermetallics. In this paper, both secondary dendritic arm spacing (SDAS) and intermetallics have been quantified as a function of grain refinements including traditional AlTiB and most recent Y refiners. Using U-net CNN machine learning algorithm, both Fe-rich intermetallics and eutectic Si have been successfully segmented from optical and SEM/EDS images. Different from traditional refiners, (AlTiB + Y) not only strengthens the refining ability but also reduces the percentage of harmful needle-like Fe-rich intermetallics and transforms flaky Si particles into fibrous morphology. Harvested from the comprehensive grain refiners, SDAS was reduced by 38.9%, the average equivalent diameter of eutectic Si was reduced by 37.9%, and Fe-rich intermetallics content was significantly reduced. In particular, the microstructure improved by AlTiB+0.05Y (0.6 wt.% AlTiB + 0.3 wt.%Y) resulted in an increase in elongation of the refined alloy to 3.9 ± 0.4%, which is 63.3% higher compared to the base alloy, while the UTS remained at the original 260 MPa. This paper offers a promising refining technology for the recycling and casting of Al-Si alloys. • Hybrid addition of Al-Ti-B and rare earth element Y can achieve improved refining effect. • The morphology of eutectic Si particles and Fe-rich intermetallics can be modified by new grain refiners. • Quantitative information of particle distribution can be obtained by U-net CNN machine learning algorithm • Transforming flaky Si particles into fibrous eutectics and reducing harmful β-Fe size significantly improve the properties. • Combined with thermodynamics, Fe-rich intermetallics reduction mechanism is revealed.

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