The wear behavior of multi-walled carbon nano-tubes(MWCNTs)reinforced copper metal matrix composites(MMCs)processed through powder metallurgy(PM)route was focused on and further investigated for varying MWCNT quantity...The wear behavior of multi-walled carbon nano-tubes(MWCNTs)reinforced copper metal matrix composites(MMCs)processed through powder metallurgy(PM)route was focused on and further investigated for varying MWCNT quantity viaexperimental,statistical and artificial neural network(ANN)techniques.Microhardness increases with increment in MWCNTquantity.Wear loss against varying load and sliding distance was analyzed as per L16orthogonal array using a pin-on-disctribometer.Process parameter optimization by Taguchi’s method revealed that wear loss was affected to a greater extent by theintroduction of MWCNT;this wear resistant property of newer composite was further analyzed and confirmed through analysis ofvariance(ANOVA).MWCNT content(76.48%)is the most influencing factor on wear loss followed by applied load(12.18%)andsliding distance(9.91%).ANN model simulations for varying hidden nodes were tried out and the model yielding lower MAE valuewith3-7-1network topology is identified to be reliable.ANN model predictions with R value of99.5%which highly correlated withthe outcomes of ANOVA were successfully employed to investigate individual parameter’s effect on wear loss of Cu?MWCNTMMCs.展开更多
文摘The wear behavior of multi-walled carbon nano-tubes(MWCNTs)reinforced copper metal matrix composites(MMCs)processed through powder metallurgy(PM)route was focused on and further investigated for varying MWCNT quantity viaexperimental,statistical and artificial neural network(ANN)techniques.Microhardness increases with increment in MWCNTquantity.Wear loss against varying load and sliding distance was analyzed as per L16orthogonal array using a pin-on-disctribometer.Process parameter optimization by Taguchi’s method revealed that wear loss was affected to a greater extent by theintroduction of MWCNT;this wear resistant property of newer composite was further analyzed and confirmed through analysis ofvariance(ANOVA).MWCNT content(76.48%)is the most influencing factor on wear loss followed by applied load(12.18%)andsliding distance(9.91%).ANN model simulations for varying hidden nodes were tried out and the model yielding lower MAE valuewith3-7-1network topology is identified to be reliable.ANN model predictions with R value of99.5%which highly correlated withthe outcomes of ANOVA were successfully employed to investigate individual parameter’s effect on wear loss of Cu?MWCNTMMCs.
