Effect of Mn Concentration on Mechanical Properties of A356 Aluminum Alloy Wheels Produced by Low-Pressure Die Casting

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Davut, Kemal
Gokelma, Mertol

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Abstract

Secondary aluminum alloys in automotive industry have been rising in last decades; however, the iron content is still a concern whether recycled or high iron containing aluminum alloys can fulfill the mechanical requirements. As the proportion of recycled scrap increases in aluminum alloy components, the mixing and accumulation of impurities become significant issues. In this study, manganese was used to counteract the detrimental effects of iron. Accordingly, A356 alloy automobile wheels containing 0.002 wt%, 0.040 wt%, 0.069 wt%, and 0.14 wt% Mn were cast using the low-pressure die casting method, followed by T6 heat treatment. Optical microscope (OM) examinations were performed to observe intermetallics. Additionally, the mechanical properties of the produced wheels were evaluated through hardness measurements, tensile, and Charpy impact tests. After the Charpy impact test, fractured surfaces were examined using scanning electron microscopy (SEM). Micrographs from SEM and OM were quantified using digital image processing. To interpret this extensive dataset, a statistical model was developed using microstructural data as input through multiple linear regression analysis and analysis of variance. The results were discussed together with the sensitivity analysis. A weak negative linear correlation between Mn concentration and mechanical properties was found, indicating that Mn addition is not the primary factor for the observed decrease in mechanical properties. Elongation and yield strength were significantly influenced by both aspect ratio and particles/mm2, with greater sensitivity to particles/mm2. Additionally, impact energy was strongly affected by aspect ratio of particles (intermetallics and eutectic Si) and their concentration per unit area.

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A356 Al-alloy, mechanical properties, microstructure, multiple linear regression analysis, low-pressure die casting, sensitivity analysis

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19

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2199

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