Article ID Journal Published Year Pages File Type
9796613 Materials Science and Engineering: A 2005 10 Pages PDF
Abstract
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.
Related Topics
Physical Sciences and Engineering Materials Science Materials Science (General)
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