Optimum Selection of Coated Piston Rings and Thrust Bearings in Mixed Lubrication for Different Lubricants Using Machine Learning

Abstract

The purpose of this study is to build a parametric algorithm combining analytical results and Machine Learning in order to improve the tribological performance of coated piston rings and thrust bearings in mixed lubrication using different synthetic lubricants. The friction models for piston ring conjunction and pivoted pad thrust bearing consider the basic lubrication theory, the detailed contact geometry and the complete lubricant action for a wide range of speeds. The data produced from the analytical solutions are used as input for the training of regression models. The effect of TiN, TiAlN, CrN and DLC coatings on friction coefficient are investigated through multi-variable quadratic regression and support vector machine models. The optimum selection is considered when the minimum friction coefficient is predicted. Smooth TiN2 and TiAlN coatings seem to affect better the ring friction coefficient than rougher steel, TiN1 and CrN coatings using an uncoated or coated Nickel Nanocomposite (NNC) cylinder. Using an NNC cylinder for better durability, the friction coefficients were found to be higher by 31.3−58.8% for all the studied rings due to the rougher surface morphology. On the other hand, the results indicate that pads coated with DLC show lower friction coefficients compared to the common steel and TiAlN, CrN, and TiN applications. The multi-variable second-order polynomial regression models were demonstrated to be 1−6% more accurate than the quadratic support vector machine models in both tribological contacts.