Prediction of tribological performance of Cu-Gr-TiC composites based on response surface methodology and worn surface analysis

In the current study, the prediction of tribological performance of Cu-Gr-TiC composites and its correlation with surface topography has been studied. For this purpose, the Cu-Gr composites reinforced with TiC ceramic particles were prepared via the powder metallurgy route. The prepared composites microstructure, mechanical characteristics, and dry sliding wear behaviour were assessed. A pin on disc setup was taken for tribological testing where sliding velocity is 1.5 m s-1. Wear behaviour of composites was examined using a central composite design (CCD) with three levels. The wear behavior optimization was accomplished through the utilization of response surface methodology (RSM). The input parameters in RSM consisted of sliding distance, varying load, and weight percentage (wt%) of reinforcements, while the wear rate and coefficient of friction served as the two responses. An analysis of variance (ANOVA) using RSM was conducted to identify the significant parameters influencing the wear rate and coefficient of friction. A quadratic model was suggested based on best fit and a regression equation was established for predicting the tribological properties at any given input parameter. Comparative of experimental and predicted values show close tolerance. It was observed that RSM is significant tool for predicting and optimizing the tribological properties. The composite having 3.08 wt% of TiC particles was optimized for minimum wear rate & COF at 20 N load and 2000 m sliding distance.

Automated summary

The study focuses on predicting the tribological performance of Cu-Gr-TiC composites and its correlation with surface topography. Cu-Gr composites reinforced with TiC ceramic particles were prepared using powder metallurgy. The evaluation included microstructure, mechanical characteristics, and dry sliding wear behavior. Response surface methodology (RSM) was used for wear behavior optimization and a quadratic model was suggested for predicting tribological properties. The study demonstrates the significance of RSM in predicting and optimizing tribological properties.