الفهرس 
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Abstract
In the present study, A356 aluminum alloy was reinforced with SIC particles using rheocasting route. This route exhibits many advantages when compared with the traditional liquid metallurgy processing techniques such as stir casting or vortex method. In addition to the monolithic matrix alloy A356, many composite alloys having different weight percents of SIC particles of various sizes were prepared. Three different SIC ceramic particles having average size of 30, 70 and 130 um were used as reinforcements. The SIC particles were incorporated into the A356 alloy with 10% and 20% weight. The effect of the sic particles size and weight percent on the microstructure, aging behavior, tensile properties and wear resistance were evaluated. To study the effect of temperature on the mechanical properties of the prepared composites, the tensile and wear behavior tests were carried out at elevated temperature. The tensile tests were carried out at elevant temperature. The tensile tests were carried out at room, 150 and 300c. While the wear tests were conducted at room,100,200 and 250 C.
Examination of the microstructure of the composites showed that the porosity content of the composites is higher than the matrix alloy. Increasing both the weight percent and size of SIC particles increases the overall porosity content of the composites. It was found also that the distribution of SIC particles becomes more uniform in the matrix as their weight percent increases. Moreover, increasing the particles weight percent reduces the particles agglomeration size.
The hardness test results prevailed that,incorporation of SIC particles into A356 alloy increases the haedness of the alloy. The hardness of the composites increases with increasing of both weight percent and SIC particles size. An accelerated aging response was observed in composites. In general, it was found that the addition of SIC particles accelerates the aging kinetics and reduces the time required to reach the peak hardness. The aging behavior of the compositesis found to be dependent on the amount and the size of SIC particles presented in the composite alloy. Increasing the weight percent of SIC reduces the aging time required to reach peak hardness value of the composite. In contrast, it was found that small Sic particles are more effective in acceleration of the aging kinetics when compared with the large particles.
The tensile tests carried out in this investigation showed that the performance of the composities is strongly depending on the SIC particles size, weight percent and the test temperature. At room temperature, the A356 monolithic alloy is stronger than the composites having relatively large SIC particles sizes 130 and 70 um, while the composites reinforced with 30 um Sic particles showed higher values of both ultimate tensile and yield strengths. The composities containing 10 and 20 wt.% of SIC having size of 30 um are stronger than the unreinforced A356 alloy at 150 C. Tensile tests carried out at 300C showed that there is no significant difference between the tensile properties of both the composites and A356 monolithic alloy.
Dry sliding wear behavior of A356 aluminum alloy composities reinforced with the SIC particles were investigated by using a pin-on-disc wear tester. Pins of the composites were rubbed against a 316 stainless steel disc at a constant sliding velocity of 0.95 m/s and various applied pressures. At room temperature and at constant load, the wear resistance of composites was greatly improved by increasing both the weight percent and the size of SIC particles. The results obtained from wear tests carried out at 100 and 200 c showed that, the composited showed better wear resistance as compared to the unreinforced A356 matrix alloy. The composites exhibited higher values of both the transition load and transition temperature when compared to the matrix alloy. At 250 C there is no noticeable difference in the wear resistance of the monolithic and the composites.
In the present investigation , artificial neural network ANN approach was used to predict the wear beaviour of A 356/SIC particulate metal matrix composites MMC`S.ANN model was obtained to aid in prediction and optimization of the wear rates of the composites. The results have shown that ANN is an effective tool in the prediction of the properties of PMMC`s, and quite useful instead of time consuming experimental processes.