Experimental study of the effect of temperature on mechanical properties of aluminum sheets produced by equal channel angular rolling process
The equal channel angular rolling (ECAR) process is one of the methods of severe plastic deformation that is considered to obtain ultrafine-grain materials (UFG.( In this study, the effect of temperature on the mechanical properties of 5083 aluminum alloy was investigated in this process. Equal channel angular rolling process was performed at the temperatures 25℃ (room temperature), and 200 and 300℃. The equal channel angular rolling was used as a lubricant. The evaluation of mechanical properties and fracture mode of the samples was assessed using uniaxial tensile test, micro-hardness test and scanning electron microscope (SEM). The results revealed that by increasing number of ECAR passes at a specific temperature, yield strength, ultimate tensile strength and micro-hardness increased in which that increasing rate is more higher at the initial passes than the last ones, while the elongation decreased, contrarily. Study of the mechanical properties of the samples after applying the ECAR process in a specified pass at different temperatures showed that by increasing the temperature, the yield strength, ultimate tensile strength and micro-hardness of samples decreased in comparison with the samples processed in the ambient temperature. However, the elongation was improved. As a quantitatively results, at the end of the third pass at 300°C, the yield strength and micro-hardness of the samples decreased by 14.8% and 8.5%, respectively, in comparison with the samples processed in the ambient temperature, and the elongation increased by 22.2%.
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Investigating the Fracture Toughness and Mechanical Properties of the Two-Layer Al 1050/Mg AZ31B Sheets Fabricated by the Roll Bonding, Considering the Annealing Temperature Effect
Payam Tayebi, *
Iranian Journal of Materials science and Engineering, Mar 2025 -
Detection and measurement of warping in FDM additive manufacturing process using artificial intelligence and machine vision
Ali Maghamfar, Mohammad Shahbazi *,
Iranian Journal of Manufacturing Engineering,