Showing 2 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.
M. Ghalambaz, M. Shahmiri,
Volume 5, Issue 3 (Summer 2008 2008)
Abstract
Abstract: Cooling slope-casting processing is a relatively new technique to produce semisolid cast
feedstock for the thixoforming process. Simple equipment, ease of operation, and low processing
costs are the main advantages of this process in comparison with existing processes such as
mechanical stirring, electromagnetic stirring, etc. The processing parameters of cooling slope
casting are length, angle and the material of the inclined plate and their combinations, which
usually affect the micro structural evolutions of the primary solid phase.
In order to clarify the effect of the processing parameters on the evolution of the particle size,
based on experimental investigation, Artificial Neural Network (ANN) was applied to predict the
primary silicon crystals (PSCs) size of semisolid cast ingot via a cooling slope casting process of
Al-20%(wt.%) Si alloy.
The results demonstrated that the ANN, with 2 hidden layers and topology (4, 3), could predict the
primary particle size with a high accuracy of 94%. The sensitivity analysis also revealed that
material of the cooling slope had the largest effect on particle size.
