Loading...

An investigation into the effect of alloying elements on the recrystallization behavior of 70/30 brass

Shafiei, A. M ; Sharif University of Technology | 2010

597 Viewed
  1. Type of Document: Article
  2. DOI: 10.1007/s11665-009-9516-5
  3. Publisher: 2010
  4. Abstract:
  5. An Artificial Neural Network (ANN) model has been designed for predicting the effects of alloying elements (Fe, Si, Al, Mn) on the recrystallization behavior and microstructural changes of 70/30 brass. The model introduced here considers the content of alloying elements, temperature, and time of recrystallization as inputs while percent of recrystallization is presented as output. It is shown that the designed model is able to predict the effect of alloying elements well. It is also shown that all alloying elements strongly affect the recrytallization kinetics, and all slow down the recrystallization process. The effect of alloying elements on the activation energy for recrystallization has also been investigated. The results show that Si is the element which increases the activation energy
  6. Keywords:
  7. 70/30 Brass ; ANN modeling ; Artificial neural network models ; Designed models ; Microstructural changes ; Recrystallization process ; Recrystallizations ; Activation energy ; Alloying ; Brass ; Manganese ; Manganese compounds ; Neural networks ; Recrystallization (metallurgy) ; Alloying elements
  8. Source: Journal of Materials Engineering and Performance ; Volume 19, Issue 4 , June , 2010 , Pages 553-557 ; 10599495 (ISSN)
  9. URL: http://link.springer.com/article/10.1007%2Fs11665-009-9516-5