INFLUENCE OF HEAT TREATMENT ON ELECTRO-MECHANICAL PROPERTIES OF ALUMINUM COMPOSITES

Abstract

This study presents a detailed exploration of aluminum-based composites reinforced with Al2O3 and ZnO nanoparticles, employing a customized stir method, with a primary emphasis on investigating their electro-mechanical properties. Two distinct composites were developed: Al MMC-01 (97.5 wt. % Al, 2.5 wt. % Al2O3) and Al MMC-02 (95 wt. % Al, 2.5 wt. % Al2O3, 2.5 wt. % ZnO). Microstructure analysis through SEM affirmed the uniform dispersion of Al2O3 and ZnO within the metal matrix composites. The addition of 2.5% Al2O3 notably enhanced the hardness, flexural strength, and impact toughness of the Al composite compared to pure Al. However, Al MMC-02, with an additional 2.5 wt. % ZnO and 2.5 wt. % Al2O3, exhibited increased Vickers microhardness but decreased impact strength, flexural strength, flexural modulus, and electrical conductivity compared to Al MMC-01. SEM fractured surface analysis revealed the brittle nature of Al MMC-02, characterized by cleavage cracks, deep shear dimples, and crystallographic planes. Subsequent heat treatment at solution temperatures of 510°C, 530°C, and 550°C, coupled with thermal aging between 140°C and 220°C, aimed to enhance hardness and electrical conductivity. Results revealed peak increases in Vickers microhardness (25.92% for Al MMC-01, 17.6% for Al MMC-02) and electrical conductivity (9.57% for Al MMC-01, 12.12% for Al MMC-02) at a common solution temperature of 530°C compared to the as cast state. The study developed two non-linear mathematical models using a central composite design to predict heat treatment effects on Vickers microhardness (HV) and electrical conductivity (%IACS), achieving R2 values of 89.29% and 91.50%, respectively. Characterizing specific wear rates for Al MMC-01, considering parameters like applied load, sliding speed, and duration using Taguchi's Technique, identified applied load as the most impactful factor on the specific wear rate. The study developed a highly accurate regression equation to predict specific wear rates, yielding R2 and adj R2 values of 99.85% and 99.76%, respectively. Two confirmation experiments demonstrated minimal errors between experimental and predicted values (2.1% and 6.6%).