Regression modelling on wear behaviour of nano fly ash–aluminium alloy matrix composites

Abstract

The demand for particle-reinforced composites has been increasing progressively. Hence, there is an exigency to use low-cost particles or solid waste particles as a reinforcement phase in order to reduce the cost of the composite. Fly ash is a solid waste consisting of various oxides such as silicon dioxide (SiO 2 ), aluminium oxide (Al 2 O 3 ) and iron (III) oxide (Fe 2 O 3 ), which will contribute to improving wear resistance. Hence, an attempt is made to use nanostructured fly ash as particle reinforcement to produce high-wear-resistance composite materials. Microsized fly ash was converted to nanostructured material by high-energy ball milling, and the crystallite size was reduced to 27 nm after 30 h of milling. The influence of applied load, sliding speed, sliding time and fly ash reinforcement on dry sliding wear of the metal matrix composites is determined. Wear resistance was increased with increase in fly ash fraction, and beyond 10 wt%, it was decreased. The increased frictional thrust at higher load results in increased debonding and wear rate. The regression model was developed by using the statistical software Minitab R17.1.0 to predict the wear behaviour of the composites under conditions of different normal loads and time periods. The model has been validated, and a good agreement was observed.