NEURAL NETWORK PREDICTION OF THE EFFECT OF SEMISOLID METAL (SSM) PROCESSING PARAMETERS ON PARTICLE SIZE AND SHAPE FACTOR OF PRIMARY a-AI ALUMINUM ALLOY A356.0

Problems such as the difficulty of the selection ofprocessing parameters and the largequantity 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 theprocessing parameters, (i.e. shearing rate-time-temperature) combinations on particle size andshape factor, based on experimental investigation, the artificial neural network (ann) wasapplied to predict particle size and shape factor ssm processed aluminum a.356.0 alloy. theresults clearly demonstrated that, the ann with 2 hidden layers and topology (4, 2) can predict theshape factor and the particle size with high accuracy of94%. the sensivity analysis also revealedthat 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.