Influence of Al2O3 Nanoparticles Addition in ZA-27 Alloy-Based Nanocomposites and Soft Computing Prediction

Three different and very small amounts of alumina (0.2, 0.3 0.5 wt. %) in two sizes (approx. 25 100 nm) were used to enhance the wear characteristics ZA-27 alloy-based nanocomposites. Production was realised through mechanical alloying pre-processing compocasting processes. Wear tests under lubricated sliding conditions on a block-on-disc tribometer, at speeds (0.25 1 m/s), normal loads (40 N) distance 1000 m. Experimental results analysed by applying response surface methodology (RSM) suitable mathematical model for rate tested nanocomposites developed. Appropriate maps constructed mechanism is discussed this paper. The accuracy prediction evaluated with use an artificial neural network (ANN). architecture ANN 4-5-1 obtained overall regression coefficient 0.98729. comparison predicting methods showed that more efficient wear.