Showing 1 results for M. Ghalambaz,
M. Ghalambaz,, M. Shahmiri, Y. H. K Kharazi,
Volume 4, Issue 1 (winter & spring 2007 2007)
Abstract
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.
