Comparative study of wear behaviour of ZA37 alloy, ZA37/SiC composite, and grey cast iron under lubricated conditions: Predictive modeling by machine learning

Abstract

In this study, sliding wear characteristics of ZA37 alloy, ZA37/SiC composite, and grey cast iron were evaluated under lubricated conditions using base oil and base oil blended with 5 wt% graphite particles (size ranging from 10 µm to 100 µm). Wear tests revealed that reinforcing ZA37 alloy with silicon carbide (SiC) particles substantially enhances the wear resistance. The best wear performance was observed with base oil blended with 10 µm graphite particles. The response surface methodology (RSM) results revealed the significance of process parameters on wear rate. It was found that load exerted the highest influence on wear reduction. The study's findings show that the RF model (R² = 85 %) demonstrates its superior capacity to elucidate the variability in the test data compared to the SVR model (R² = 62 %).