Artificial Neural Network prediction of Cu–Al 2O 3 composite properties prepared by powder metallurgy method

摘要

Artificial Neural Networks (ANNs) are excellent tools for prediction of complex processes that have many variables and complex interactions. In the present study, the properties of copper based composite prepared from sintering of mechanically alloyed powders, were predicted using Artificial Neural Network (ANN) approach. In order to prepare copper based composites, copper powder with four different amounts of Al2O3reinforcement (1, 1.5, 2, 2.5wt%) were mechanically alloyed and the consolidated compacts of prepared powders were sintered in five different temperatures of 725–925°C at seven several sintering times of 15–180min. Hardness and electrical conductivity measurements were performed to evaluate the properties of these composites. Then, for modeling and prediction of hardness and electrical conductivity, a multi layer perceptron back propagation feed forward neural network was constructed to evaluate and compare the experimental calculated data to predicted values. It was found that, in a given sintering temperature of 875°C, the electrical conductivity increases as the sintering time increases and the amount of Al2O3reinforcement decreases. Also, increasing of reinforcement amount and sintering time in a given sintering temperature of 875°C leads to a decrease in hardness. Furthermore, electrical conductivity and hardness of specimens have shown a consistency with predicted results of ANN. These trained values had an average error of 3% and 5% for electrical conductivity and hardness values, respectively.