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

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

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Three different and very small amounts of alumina (0.2, 0.3 and 0.

5 wt.%) in two sizes (approx.25 and 100 Purse Fabrics nm) were used to enhance the wear characteristics of ZA-27 alloy-based nanocomposites.

Production was realised through mechanical alloying in pre-processing and compocasting processes.Wear tests were under lubricated sliding conditions on a block-on-disc tribometer, at two sliding speeds (0.25 and 1 m/s), two normal loads (40 and 100 N) and a sliding distance of 1000 m.

Experimental results were analysed by applying the response surface methodology (RSM) and a suitable mathematical model for the wear rate of tested nanocomposites was developed.Appropriate wear maps were constructed and the A wear mechanism is discussed in this paper.The accuracy of the prediction was evaluated with the use of an artificial neural network (ANN).

The architecture of the used ANN was 4-5-1 and the obtained overall regression coefficient was 0.98729.The comparison of the predicting methods showed that ANN is more efficient in predicting wear.