In this study, Al2O3/SiC particulate reinforced (aluminium matrix composites) AMCs, which was produced by using stir casting
process, bending strength and hardening behaviour were obtained using a back-propagation neural network that uses gradient
descent learning algorithm. First, to prepare the training and test (checking) set of the network, some results were experimentally
obtained and recorded in a file on a computer. In the experiments, dual ceramic powder mix was prepared by chemical route then
inserted in A332 alloy in melt condition by stir casting process. While SiC particles were supplied commercially, Al2O3 was
chemically produced from aluminium sulphate. Al2O3/SiC powder mix was prepared, and heated up in ceramic crucible in the
furnace. Then Al2O3/SiC ceramic cake was produced [1]. This ceramic cake was milled to adjust particle size before stir casting. This
dual ceramic powder with different SiC particle size range was added into liquid matrix alloy. The effect of the reinforcing particle
size on bending strength and hardness resistance of Al2O3/SiC reinforced AMCs was investigated. Mechanical tests showed that
bending strength and hardness resistance of 10 vol% Al2O3/SiC dual ceramic powder composites decrease with increasing reinforced
SiC particle size. In neural networks training module, different SiC (lm) particles size range was used as input and bending strength,
hardening behaviour and also porous properties in the produced AMCs were used as outputs. Then, the neural network was trained
using the prepared training set (also known as learning set). At the end of the training process, the test data were used to check the
system accuracy. As a result the neural network was found successful in the prediction of bending strength, hardness behaviour and
also porous properties for any given SiC (lm) particles size range in the produced AMCs.
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