Characterization of surface morphology, wear performance and modelling of graphite reinforced aluminium hybrid composites

In this work, morphological characterization and wear behaviour of micro graphite (Gr) and nano zirconia (ZrO2) reinforced aluminium (Al) hybrid composites were investigated. The amounts of Gr (0, 2, 4, 6 and 8 wt%) were added to Al + 10 wt% ZrO2 composites through powder metallurgy (PM) technique. The morphological characterization of all synthesized composites was performed using X-ray diffractometer (XRD), Scanning Electron Microscope (SEM), Energy Dispersive Spectrum (EDS) and Elemental Maps. The influence of Gr (0, 2, 4, 6 and 8 wt%) reinforcement, sliding distance (400, 600 and 800 m) and applied load (30 and 40 N) were studied using the design of experiments (5 x 3 x 2). The contribution of Gr reinforcement, sliding distance and applied load were found to be 80.59, 16.25 and 2.06% respectively. The wear resistance of the hybrid composite reinforced with 6 wt% graphite particles is maximum amongst all synthesized composites. Worn surfaces and wear debris were analyzed using both SEM and EDS to recognize the wear mechanism. EDS spectrum of hybrid composites validated the existence of oxides of aluminium and iron, known as Mechanically Mixed Layer (MML), responsible for enhanced wear resistance. This investigation revealed that wear resistance of the hybrid composites can be enhanced by the combined effect of Gr and ZrO2. Further, five different regression models were developed to estimate the wear behaviour of composites with graphite (Gr) reinforcement, sliding distance and applied load as inputs. The accuracy of the models was evaluated using the five most commonly used statistical indicators. The models were ranked according to their suitability of prediction using Global Performance Indicator (GPI). The predicted values and experimental data showed a high degree of association (Correlation coefficient, R ranging from 0.91 to 0.96). It was recognized that the developed models can be used to predict the wear behaviour within the range of investigation. (C) 2019 Karabuk University. Publishing services by Elsevier B.V.