Prediction of effect of fabrication parameters on the properties of B4C ceramic particle reinforced AA2024 matrix nanocomposites using neural networks

In the present study, a prediction model was created to predict the impact of B4C content, milling time and matrix size on the hardness and density of AA2024-B4C nanocomposites produced by means of flake powder metallurgy. The process involved synthesizing AA2024-B4C nanocomposite powders by mechanical milling followed by preparing nanocomposites by hot pressing. The matrix size of the AA2024 alloy powders, the milling time, and the B4C content were selected as the input parameters. On the other hand, the density and the hardness of the AA2024-B4C nanocomposites were designed as the output parameters. The results showed that the sintered density is significantly improved by decreasing matrix size. It was found that density of AA2024–3 wt%B4C nanocomposites is lower than AA2024–1 wt%B4C nanocomposites. However, the hardness of AA2024–3 wt%B4C nanocomposites is better than AA2024–1 wt%B4C nanocomposites. The AA2024-B4C nanocomposite prediction model could predict hardness and density with a mean absolute percentage error (MAPE) of 0.32% and 1.38%. The results show that the ANN model is a useful tool for the matrix size effect prediction, milling time and B4C content on the density and hardness of AA2024-B4C nanocomposites prepared by flake powder metallurgy.