Prediction of grain size of Al–7Si Alloy by neural networks

Neural networks, which are known for mapping non-linear and complex systems, have been used in the present study to model the grain-refinement behavior of Al–7Si alloy. The development of a feed forward neural network (FFNN) model with back-propagation (BP) learning algorithm has been presented for the prediction of the grain size, as a function of Ti and B addition level and holding time during grain refinement of Al–7Si alloy. Comparison of the predicted and experimental results shows that the FFNN model can predict the grain size of Al–7Si alloy with good learning precision and generalization.