Modeling of tribological properties of alumina fiber reinforced zinc–aluminum composites using artificial neural network

Abstract

It is known that the strength of a metal, as well as wear resistance can be successfully improved by fiber reinforcement. In this study, multiple-layer feed-forward artificial neural network (ANN) modeling for tribological behavior of short alumina fiber reinforced zinc–aluminum composites has been established. The specific wear rate and coefficient of friction obtained from a series of the wear tests were used in the formation of training sets of ANN. Samples of composite material with 10, 15, 20 and 30vol.% fiber contents were prepared by the pressure die-casting method. Wear tests with pin-on-disc arrangement had performed at a constant sliding speed of 1m/s under four different loads (5, 10, 20 and 40N). The results of experimental tests showed that wear behavior and friction coefficient of the composites were significantly affected by the fiber volume fraction. The specific wear rate decreased with increasing fiber volume fraction and increased with increasing load. The coefficients of friction of the composites were higher than that of the unreinforced matrix alloy. The modeling results confirm the feasibility of the ANN and its good correlation with the experimental results. The degrees of accuracy of the prediction were 94.2 and 99.4% for specific wear rate and friction coefficient, respectively. It is concluded that ANN is an excellent analytical tool if it is well trained. This means considerable cost and time saving. Finally, using ANN modeling data and experimental data, 3D plots and empirical expressions for specific wear rate and friction coefficient related to load and fiber volume fraction were established.