Abstract: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 via experimental, statistical and artificial neural network (ANN) techniques. Microhardness increases with increment in MWCNT quantity. Wear loss against varying load and sliding distance was analyzed as per L16 orthogonal array using a pin-on-disc tribometer. Process parameter optimization by Taguchi’s method revealed that wear loss was affected to a greater extent by the introduction of MWCNT; this wear resistant property of newer composite was further analyzed and confirmed through analysis of variance (ANOVA). MWCNT content (76.48%) is the most influencing factor on wear loss followed by applied load (12.18%) and sliding distance (9.91%). ANN model simulations for varying hidden nodes were tried out and the model yielding lower MAE value with 3-7-1 network topology is identified to be reliable. ANN model predictions with R value of 99.5% which highly correlated with the outcomes of ANOVA were successfully employed to investigate individual parameter’s effect on wear loss of Cu-MWCNT MMCs.

Key words: copper; multi-walled carbon nano-tube (MWCNT); powder metallurgy; wear; Taguchi method; analysis of variance (ANOVA); artificial neural network