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In this study a feed forward back propagation artificial neural network (ANN) model was established to predict Vickers microhardness in aluminum-alumina nanocomposites which have been synthesized by mechanical alloying and hot pressing. Volume percent of reinforcement, size of nanoparticles, force in microhardness test; and mechanical alloying parameters, such as time, ball to powder ratio (BPR) and speed of ball mill were used as the inputs and Vickers microhardness as the output of the model. Effective parameters in training such as learning rate, hidden layers and number of neurons, were determined by trail and error due to amount and percentage of errors. Regression analysis in train, validation and test stages; and mean squared error were used to verify the performance of neural network. Average error of predicted results was 2.67% or 2.25 Vickers. Also mean squared error for validation data was 7.76. As can be expected, ANN methods reduce the expenses of experimental investigations, by predicting the optimum parameters.

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The defectless microstructure of metal matrix composites, the uniform distribution of particles and their good properties are determined by the production parameters and the base material and reinforcement. In this study, high-energy planetary ball mill was used to fabricate Al6063-SiC composite powder. Aluminum chips were milled with different time and ball to powder weight ratio (BPR) in high energy planetary ball mill. The resulting powder was mechanically alloyed by adding different weight percentages of silicon carbide (SiC) and BPRs at different times. During the milling process under argon atmosphere, stearic acid was used as a process control agent (PCA) to prevent excessive cold welding and agglomeration of the powder. After mechanical alloying, the effect of alloying time, BPRs and weight percentage of silicon carbide, on the obtained composite powder were examined morphologically by particle size analysis (PSA), field emission scanning electron microscope (FESEM), and the fuzzy compounds by X-ray diffraction (XRD) spectroscopy. According to the X-ray diffraction pattern of the samples, grain size was calculated using the Williamson-Hall model. The results of mechanical milling and alloying process have shown that in short milling times with high BPRs composite powder with finer particle size could be achieved. Also, the presence of silicon carbide reinforcing particles accelerates the process of mechanical alloying and further reduces the particle size.