Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9796613 | Materials Science and Engineering: A | 2005 | 10 Pages |
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)
Authors
N.S. Reddy, A.K. Prasada Rao, M. Chakraborty, B.S. Murty,