A Statistical Model For Determining Friction And Wear Of SK11 Ball Bearing In Nanoparticles-Enhanced Engine Oil

Alias, Nor Hasrin Nadia (2017) A Statistical Model For Determining Friction And Wear Of SK11 Ball Bearing In Nanoparticles-Enhanced Engine Oil. Project Report. UTeM, Melaka, Malaysia. (Submitted)

Text (24 Pages) A Statistical Model For Determining Friction And Wear Of SK11 Ball Bearing In Nanoparticles-Enhanced Engine Oil.pdf - Submitted Version Download (1MB)

Abstract

Hexagonal Boron Nitride is one of nanoparticles types that widely used in tribology industry for improvement in tribological performance. In order to reduce the COF and wear scar of ball bearing, hBN is added into the engine oil. So, the Four-Ball Tester experiment is running in order to predict the friction and wear reduction of SK11 ball bearing. The experiment is carried with loads of 100N, 300N, 500N, speed of 100 rpm, 300 rpm, 500 rpm and temperature at room temperature, 50°C and 100°C. In addition, the portable microscope will be used to test the wear scar diameter or the worn that form on the surface of ball bearing. Hence, this experiments were conducted based on Taguchi method. A L9 Orthogonal array was selected for design of experiment. The analysis that conducted for this experiment is ANOVA which is used in investigating the influence of applied load, speed and temperature on friction coefficient and wear rate. Besides, the regression model were developed for COF and wear rate. In this research, it is observed that applied load has the highest influence in COF and wear rate followed by speed and temperature. Lastly, the experimental result were validated by the confirmation tests.

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