ORIGINAL PAPER
Optimization of Friction Stir Processing Parameters of Aluminum Alloy Reinforced with Hybrid Nanoparticles Using the Taguchi Method
 
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University of Baghdad, College of Engineering, Department of Mechanical Engineering, Iraq
 
 
Online publication date: 2022-12-03
 
 
Publication date: 2022-12-01
 
 
International Journal of Applied Mechanics and Engineering 2022;27(4):13-25
 
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ABSTRACT
This study deals with the selection of optimum parameters for friction stir processing of Al alloy 6061-T6 reinforced with a hybrid nanoparticle (B4C and SiO2) in terms of their effect on the mechanical properties (hardness, tensile strength, and wear resistance) using Taguchi method. This work was carried out under four parameters each one running in three levels; rotational speeds (800, 1000 and 1200) rpm, travel speeds (10, 20, and 30) mm/min, holes depth (2, 2.5, and 3) mm, and mixing ratio of (SiO2/B4C) nanoparticles (1/1, 1/2, and 1/3), using L9 (34) Taguchi orthogonal array. Tensile strength and microhardness tests were conducted to evaluate the mechanical properties, in addition to the wear resistance test which is carried out using a pin-on-disk device. The microstructure was examined by optical microscopy, field emission scanning electron microscopy, and x-ray diffraction analysis. It was found that the highest tensile strength (223) MPa at 1200 rpm rotational speed, 30 mm/min traverse speed, 2.5 mm holes depth, and 1/2 (SiO2/B4C) nanoparticles mixing ratio, the highest hardness reached is (155) HV, then decreases in the direction of thermomechanically affected zone (TMAZ), heat affected zone (HAZ), and the base material at (1200) rpm rotational speed, (30) mm/min linear speed, a hole depth of (2) mm and (1/3) mixing ratio of (B4C/SiO2) nanoparticles. The wear behavior was of a mild type or an oxidative type at low loads (5 N), which became severe or metallic wear at higher loads (20 N) at fixed sliding time and speed. The (ANOVA) table has been used to determine which parameter is the most significant using MINITAB software.
 
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ISSN:1734-4492
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