Surface characterization and specific wear rate prediction of r-GO/AZ31 composite under dry sliding wear condition

The effect of reduced graphene oxide (r-GO) nanosheets on the dry sliding wear behaviour of AZ31 alloy composites produced by solvent based powder metallurgy technique has been investigated. The percentage of reinforcement addition was limited to 0.2% and 0.4%. Results show that r-GO nano-sheets considerably increases the microhardness upto 64.4 HV. The tribological behaviour of composites was investigated by pin on disc tribometer for an optimal set of control factors. Reinforcement weight percentage, load, sliding distance and sliding velocity was taken as input parameters. Taguchi coupled artificial neural network has been used to plan and analyze the experiment. Based on the study it was observed that reinforcement weight percentage and load are the most influencing factor which affects the specific wear rate. Adapted ANN results show better predictablity with R-value 99.98% and the same was effectively used to investigate the behaviour of each control factors.