Artificial Intelligence Investigation on (Al-Si-Fe) Alloy Reinforced with Nanoceramic Particles by RSM

Abstract

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.