Iranian Journal of Materials Science and Engineering
Iran University of Science & Technology
Sat, Nov 9, 2024
[Archive]
Volume 4, Issue 1 (winter & spring 2007 2007)
IJMSE 2007, 4(1): 41-47 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
M. Ghalambaz,, M. Shahmiri, Y. H. K Kharazi. NEURAL NETWORK PREDICTION OF THE EFFECT OF SEMISOLID METAL (SSM) PROCESSING PARAMETERS ON PARTICLE SIZE AND SHAPE FACTOR OF PRIMARY α-Al ALUMINUM ALLOY A356.0.. IJMSE 2007; 4 (1) :41-47
URL: http://ijmse.iust.ac.ir/article-1-115-en.html
URL: http://ijmse.iust.ac.ir/article-1-115-en.html
Abstract: (30225 Views)
Abstract: Problems such as the difficulty of the selection of processing parameters and the large
quantity of experimental work exist in the morphological evolutions of Semisolid Metal (SSM)
processing. In order to deal with these existing problems, and to identify the effect of the
processing parameters, (i.e. shearing rate-time-temperature) combinations on particle size and
shape factor, based on experimental investigation, the Artificial Neural Network (ANN) was
applied to predict particle size and shape factor SSM processed Aluminum A.356.0 alloy. The
results clearly demonstrated that, the ANN with 2 hidden layers and topology (4, 2) can predict the
shape factor and the particle size with high accuracy of 94%.The sensivity analysis also revealed
that shear rate and solid fraction had the largest effect on shape factor and particle size,
respectively. The shear rate had a reverse effect on particle size.
Type of Study: Research Paper |
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
