Aluminum alloys with ceramic reinforced particulates are made prospective in aerospace, transportation, and industrial applications ampler to their low mass density, stiffness, and high specific strength. In this work, Aluminium Alloy(AA) 7010 - TiB2 (Titanium Diboride) composites with different amounts of reinforcement (5, 7.5 and 10 wt.%) were produced by the exothermic reaction of halide salts K2TiF6 and KBF4 added in 120% excess to the stoichiometric ratio with molten AA7010.The effect and dispersion of TiB2 particulates in AA7010 were analyzed by microstructural, mechanical and corrosion behavior. The dispersion of reinforcement in the matrix alloy was analyzed by optical microscope and field emission scanning electron microscope (FESEM) images. X-ray diffraction patterns of the prepared composites reveal the formation of TiB2 particles in the matrix alloy. Indeed samples are tested according to ASTM G34 standard for ex- foliation corrosion rate by weight loss method. The result shows improved hardness, tensile strength and yield strength of composites to about 35%, 260%, and 240% respectively. The mechanical and corrosion resistance of 10% TiB2 shows better results compared with matrix alloy and other concentrations of reinforcements.
Al 4043 alloy is extensively used as a filler material for welding aluminum alloys, especially when welding alloys from the Al 6000 series. It is utilized in aerospace and automotive structural components. For longer life in automotive applications, the wear resistance of Al 4043 alloy must be improved. According to research, tungsten carbide has good wear resistance. In this research, Al 4043 alloy is reinforced with varying percentages (1, 3, and 5%) of nano-sized tungsten carbide to increase wear resistance. Taguchi L27 orthogonal array is employed for the wear analysis. The Taguchi signal-to-noise ratio is used to determine the optimal parameters for minimizing wear and coefficient of friction. The regression model and artificial neural network are developed to predict the experimental results. The outcomes of the regression model and artificial neural network are compared to the experimental results to demonstrate both models’ efficacy. A confirmation test was carried out for the optimal process parameter. The result shows that the minimized specific wear rate of 0.12 mm3/Nm, coefficient of friction of 0.01, and frictional force of 1.02 N are achieved at the optimal combination.
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