Dry sliding wear investigation of graphene reinforced copper-silica sand composite by response surface methodology. (December 2022) - Arif, Sajjad
Abstract: In the present investigation, Cu-based metal matrix composites (MMCs) have been fabricated through powder metallurgy using silica sand micro-particles and graphene as nano-fillers, and their influence on wear behavior has been studied. The weight fraction of silica sand has been fixed to 5 wt% and graphene nanoparticles have been varied from 0.1 to 0.3 wt% in the steps of 0.1 wt%. The investigated samples have been fabricated by cold pressing at 500 MPa, followed by sintering (750–900 °C × 2 h) in a nitrogen atmosphere. The wear behaviour of all fabricated samples has been done on a pin-on-disc wear tester at ambient conditions. Microstructural characterization such as Optical microscopy, X-ray diffraction (XRD), Energy dispersion spectroscopy (EDS), scanning electron microscopy (SEM), and Raman spectroscopy showed uniform dispersion of both silica and graphene nanoparticles. Relative density, hardness and wear resistance increased with the addition of graphene nanoparticles up to 0.2 wt%. The electrical conductivity of the samples has been evaluated using four probes setup. Increasing the amount of graphene nanoparticles from 0.1 to 0.3 wt% resulted in a decrease in the electrical conductivity of the sintered copper silica composite. The wear mechanism of worn surfaces has been analyzed and discussed using scanning electron microscopy. Adhesive wear, abrasive wear and delamination were the dominant wear mechanisms during the dry sliding wear test. Further, Response Surface Methodology (RSM) technique have been employed to investigate the influences of most dominating process parameters on responses like sintered density, hardness and wear loss. Finally, based on error analysis, model-predicted results and experimental data have been compared and found to be satisfactory. The percentage errors during the confirmatory test for sintered density, hardness and wear loss are 1.59, 2.06 and 2 respectively, which justify the worth of present research work. Graphical Abstract: ga1