Grain refinement behavior of an aluminum alloy by inoculation and dynamic nucleation
Source abstract
Abstract
Grain refinement offers several benefits in aluminum casting applications. Two methods are normally used to achieve a grain-refined microstructure: inoculation and dynamic nucleation. Inoculation is widely applied in industry, but is not an efficient process. Dynamic nucleation, achieved by application of localized forced convection with rapid cooling, is an alternative process. However, a deeper understanding of dynamic nucleation is required if this process is to be used commercially. This study aims to understand the grain refinement behavior of an aluminum alloy under the influences of inoculation and dynamic nucleation. A rapid quenching method was used to investigate the combined effects of inoculation and dynamic nucleation on the solid fraction, particle density and particle size of the secondary nuclei. In addition, the effects of the particle density of the secondary nuclei on the final cast microstructure were studied. The rapid quenching results show that dynamic nucleation by application of forced convection with localized cooling to the melt yields an increased solid fraction and particle density of secondary nuclei. The solid fraction and particle density are further increased by inoculation. This study also shows that increasing the convection level in an inoculated melt held at a temperature slightly above the liquidus temperature increases the effectiveness of dynamic nucleation, which consequently yields a finer microstructure of the final cast samples. The findings suggest that grain refinement can be effectively achieved by applying forced convection with localized cooling to create a low fraction solid of secondary nuclei in the melt prior to pouring and casting.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Aluminum Alloy Microstructure Properties · Aluminum Alloys Composites Properties · Microstructure and mechanical properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (20)
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