2015
DOI: 10.3139/146.111152
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Investigation of the wear resistance and microstructure of Al/SiC metal matrix composites as a function of reinforcement volume fraction and reinforcement to matrix particle size ratio applying artificial neural network

Abstract: In this study, the influences of reinforcement volume fraction and the ratio of the reinforcement particle size to the matrix particle size on the wear behaviour of Al/SiC metal matrix composites were investigated by use of a model function obtained from an artificial neural network. Hardness and ball-on-disc wear tests were applied to Al/SiC composites manufactured via a powder metallurgy method. The results indicate that as the reinforcement volume fraction and the ratio of the reinforcement particle size to… Show more

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Cited by 3 publications
(2 citation statements)
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“…This composite material with the weight loss of 0.0011 g has shown the best wear resistance. It is worthy to note that, the optimum value of volume fraction to have the best wear performance was obtained 15% in our previous study [56]. The highest mean COF value is referred to the composite with volume fraction of 20% and R s /M s ratio of 3.5.…”
Section: Wear Loss and Coefficient Of Frictionmentioning
confidence: 97%
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“…This composite material with the weight loss of 0.0011 g has shown the best wear resistance. It is worthy to note that, the optimum value of volume fraction to have the best wear performance was obtained 15% in our previous study [56]. The highest mean COF value is referred to the composite with volume fraction of 20% and R s /M s ratio of 3.5.…”
Section: Wear Loss and Coefficient Of Frictionmentioning
confidence: 97%
“…This composite material has shown the weakest wear resistance. The high value of the volume fraction increases the probability of clustering [29,56], therefore; the reinforcement particles would easily be dislodged and removed from the surface and increase the metal-metal contact. Fig.…”
Section: Wear Loss and Coefficient Of Frictionmentioning
confidence: 99%