Prediction of abrasive wear rate of in situ Cu–Al2O3 nanocomposite using artificial neural networks

In this work, artificial neural networks (ANNs) technique was used in the prediction of abrasive wear rate of Cu–Al2O3 nanocomposite materials. The abrasive wear rates obtained from series of wear tests were used in the formation of the data sets of the ANN. The inputs to the network are load, sliding speed, and alumina volume fraction. Correlation coefficients between the experimental data and outputs from the ANN confirmed the feasibility of the ANNs for effectively model and predict the abrasive wear rate. The comparison between the ANNs and the multi-variable regression analysis results showed that using ANNs technique is more effective than multi-variable regression analysis for the prediction of abrasive wear rate of Cu–Al2O3 nanocomposite materials. Optimization of the training process of the ANN using genetic algorithm (GA) is performed and the results are compared with the ANN trained without GA. Sensitivity analysis is also done to find the relative influence of factors on the wear rate. It is observed that load and alumina volume fraction effectively influence the wear rate.