Abstract

These days, metal matrix composites (MMCs) are frequently investigated by incorporating the proper reinforcement particles. In order to enhance various properties of the manufactured MMCs, reinforcement particles are embedded into the base metal. In the present investigation, A356 MMCs reinforced with 5 wt% of zirconium silicate (ZrSiO4) particles have been prepared and tested for the friction and wear behaviour. Utilizing the stir-casting technique, the composites were produced; and the test specimens have been prepared with ASTM G99 standards. Three significant process variables have been considered for performing the wear test such as normal load, sliding velocity and sliding distance. The Taguchi’s L9 orthogonal array design has been used to plan the experimental design for the wear test. Main Effect Plots (MEPs) have been drawn for the coefficient of friction (COF) and wear rate values obtained during the test for different combinations of selected input variables, and analyzed. To determine the optimal input variable combinations that lead to lower COF and wear rate, Grey-Fuzzy Reasoning Grade Analysis (GFRGA) has been used. For the taken into account input conditions and the recorded output responses, an artificial neural network (ANN) model has been created. The surfaces of the worn out samples have been tested with the aid of the micrographs taken by a Scanning Electron Microscope (SEM).

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