Reddy, N. S. ; Rao, A. K. Prasada ; Chakraborty, M. ; Murty, B. S. (2005) Prediction of grain size of Al-7Si Alloy by neural networks Materials Science and Engineering A, 391 (1-2). pp. 131-140. ISSN 0921-5093
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Official URL: http://www.sciencedirect.com/science/article/pii/S...
Related URL: http://dx.doi.org/10.1016/j.msea.2004.08.042
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.
| Item Type: | Article |
|---|---|
| Source: | Copyright of this article belongs to Elsevier Science. |
| Keywords: | Grain Refinement; Master Alloys; Al-7Si Alloy; Feed forward Neural Networks; Extrapolation |
| ID Code: | 73834 |
| Deposited On: | 08 Dec 2011 03:51 |
| Last Modified: | 08 Dec 2011 03:51 |
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