Synthesis and characterization of aluminium composite developed by friction stir processing

Abstract

newline ABSTRACT newlineIn recent years, extensive research on aluminium metal matrix composites (AMMCs) has been observed due to their exceptional properties, such as high strength-to-weight ratio, stiffness, wear resistance, and thermal stability. This study focuses on the development of aluminium metal matrix composites through different friction stir processing (FSP) routes and characterization of their mechanical and metallurgical properties. The objective of this thesis is to optimize the process parameter for development of the composites and make them suitable for practical applications. newlineThe objective of this research endeavor is to investigate the utilization of friction stir processing (FSP) in the fabrication of surface composites comprising AA2219 aluminum and reinforcement particulates Y2O3 and TiB2. The study conducts an exhaustive analysis of the effects that FSP has on microstructural characteristics, mechanical properties, and wear resistance. In determining the optimal process parameters to attain a structure devoid of defects and containing refined granules subsequent to FSP, a rotational speed of 1600 rpm and traverse velocities of 25, 40, and 60 mm/min were identified as the most favorable. There exists a positive correlation between augmenting traverse speed and both ultimate tensile strength (UTS) and hardness, as well as a decrease in particle size. newlineIn subsequent investigations, aluminum composites were fabricated using friction stir deposition (FSD) methods. These methods involved the utilization of a consumable rod with two to four perforations, a spindle speed of 2500 rpm, and a traversal speed of 5 mm/min. The composites that were formed and deposited onto an aluminum substrate demonstrated an increase in hardness of around 85 to 116 HV, depending on the configuration of the holes and the reinforcement particles. newlineRegression models and predictive models, such as the Adaptive Neuro-Fuzzy Inference System (ANFIS), were utilized to forecast the mechanical characteristics of Al-Y2O3 composit