Prediction of effect of volume fraction, compact pressure and milling time on properties of Al-Al2O3 MMCs using neural networks

Abstract

An artificial neural network (ANN) model was developed to predict the effect of volume fraction, compact pressure and milling time on green density, sintered density and hardness of Al-Al2O3 metal matrix composites (MMCs). Al-Al2O3 powder mixtures with various reinforcement volume fractions of 5, 10, 15% Al2O3 and milling times (0 h to 7 h) were prepared by mechanical milling process and composite powders were compacted at various pressure (300, 500 and 700 MPa). The three input parameters in the proposed ANN were the volume fraction, compact pressure and duration of the milling process. Green density, sintered density and hardness of the composites were the outputs obtained from the proposed ANN. As a result of this study the ANN was found to be successful for predicting the green density, sintered density and hardness of Al-Al2O3 MMCs. The mean absolute percentage error for the predicted values didn’t exceed 5.53%. This model can be used for predicting Al-Al2O3 MMCs properties produced with different reinforcement volume fractions, compact pressures and milling times.