Surface coatings analysis and their effects on reduction of tribological properties of coated aluminum under motion with ML approach

Abstract

Abstract The popularity of coated aluminum is gaining significant attention in numerous sectors in the industry due to its specific strength, corrosion resistance, and recyclability. However, because of friction, its lifetime reduces which causes a billion-dollar loss every year to our property. Many types of research are going around the world on how friction and wear loss can be reduced. This research focuses on the tribological study of coated aluminum in different conditions in the experiments, lubricant is used to find its efficiency, and coating materials have also its self-lubricating properties. Both reciprocating motion of pin and simultaneous motion of pin and disc applied. The combined effects of lubrication and motions are correlated with the reduction of tribological properties to a certain extent. The velocity of both pin and disc is also varied. Applied loads are changed in different experiments as well. Roughness analysis has also been done to observe the effect of lubricant, motion, and applied load on the surface of the specimens. SEM, EDX, XRD, and FTIR tests are also performed to check the morphology of the specimens. The experiments show that comparatively less friction and wear are in at lubricated, reciprocating, and less velocity of pin and disc conditions. Less coefficient of friction is observed at higher applied load but less wear is produced at lower applied load. The Machine Learning (ML) approach is used to detect patterns automatically in datasets and create models to predict future data or other outcomes.