Tensile and microstructural properties of SiC micro-particle reinforced AZ91 composites for application in light-vehicle and aerospace industries: Dataset

Published: 20 August 2024| Version 1 | DOI: 10.17632/nk4tttpzyy.1
Contributors:
Robert Otieno,
,

Description

The data is composed of 11 columns and 85 rows. It is a small dataset but was specific to research on SiC reinforced AZ91 that is growing. Data was obtained from recent research publications on the same area. The provided data summarizes the mechanical properties of various aluminum-silicon carbide (Al-SiC) composites, subjected to different treatments. The treatments include homogenization with T6 heat treatment, Equal Channel Angular Pressing (ECAP), rolling, ECAP combined with rolling, and hot extrusion. The volume percentage of SiC ranged from 0.5% to 30%, with SiC particle sizes varying from 0.2 to 105 micrometers. The average grain size of the composites was between 0.74 and 14 micrometers. Mechanical properties such as yield strength (YS), ultimate tensile strength (UTS), total percentage elongation, and Young's modulus (E) were measured, showing a wide range of values: YS from 83 to 515 MPa, UTS from 95.1 to 622 MPa, elongation from 0.5% to 18.3%, and E from 40.2 to 159.6 GPa. These variations indicate the significant impact of SiC content, particle size, and treatment on the composites' properties. The data is sourced from research articles published between 2015 and 2024, with digital object identifiers (DoIs) provided for further reference.

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Steps to reproduce

The data was found through literature review of SiC reinforced AZ91 composites. The composition of SiC in the composite by alloy code (Alloy_code), Treatment, volume percentage (SiC_vol_perc), SiC particle size in microns (SiC_size_um), average grain size in microns (av_grain_um), yield strength in MPa (YS_MPa), ultimate tensile strength (UTS_MPa), total percentage elongation (total_perc_elong), Young's Modulus (E_GPa), Source and DoI.

Institutions

Multimedia University of Kenya

Categories

Materials Science, Composite Material, Computational Materials Science, Machine Learning, Metallurgy, Magnesium Alloys, Ceramic Composite, Alloy Metallurgy, Advanced Material, High Entropy Alloys

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