Regression and Grey relational analysis on friction and wear behavior of AA6061/Al2O3/Si3N4/graphite hybrid nano composites

Abstract

Nowadays, the hybrid nano composites are chosen as suitable replacement for conventional metal matrix composites in order to overcome the drawbacks like porosity, dispersion and bonding of reinforcement in matrix material. The experiments were designed and the results were optimized using Taguchi L9 orthogonal array method with 3 factors and 3 levels. The aim of this research work was to identify the significance of Si3N4 nano particles in tribological characteristics of AA6061/Al2O3/Si3N4/graphite hybrid nano composites. The ASTM G99 standard was followed to conduct the wear test of the prepared hybrid nano composites. A statistical analysis was performed to establish a mathematical relation for wear rate and friction coefficient against the amount of Si3N4 nano particles, sliding speed and normal load. Grey relational analysis (GRA) was employed to identify the synergetic effect of input parameter on multiple response parameters (i.e. wear rate and COF). The wear mechanisms of the composites were interpreted using the micrographs obtained from Scanning Electron Microscope (SEM). The increasing wt% of Si3N4 nano particle reduced the values of friction coefficient and wear rate. The sliding speed of 1 m s−1, normal load of 10 N and the weight percentage of Si3N4 (4%) were noted as optimum combination of parameters resulted from GRA. The wear surfaces of the composites with 4 wt% Si3N4 nano pariticles were noticed with mild scratches. Shallow grooves and micro cuts were observed on the worn out surface of the composite which were tested at 4 m s−1. The plastic deformation and fracture of plateau were also found out on the worn surface.