Prediction of hardness in Al-Al2O3 nanocomposite using artificial neural network with alternation in effective parameters of mechanical alloying method

In this study a feed forward back propagation artificial neural network (ANN) model was established to predict Vickers microhardness in aluminum-alumina nanocomposites which have been synthesized by mechanical alloying and hot pressing. Volume percent of reinforcement، size of nanoparticles، force in microhardness test; and mechanical alloying parameters، such as time، ball to powder ratio (BPR) and speed of ball mill were used as the inputs and Vickers microhardness as the output of the model. Effective parameters in training such as learning rate، hidden layers and number of neurons، were determined by trail and error due to amount and percentage of errors. Regression analysis in train، validation and test stages; and mean squared error were used to verify the performance of neural network. Average error of predicted results was 2. 67% or 2. 25 Vickers. Also mean squared error for validation data was 7. 76. As can be expected، ANN methods reduce the expenses of experimental investigations، by predicting the optimum parameters.