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.

Physics-Informed Machine Learning—An Emerging Trend in Tribology

“…Furthermore, AI and ML can contribute to designing and optimizing tribo-systems within vast design spaces [16] or can even contribute to discovering novel solutions that may not have been considered previously. All of these aspects may lead to the development of more efficient lubricants [17,18] and materials [19,20], advanced surface modifications [21,22], manufacturing processes [23,24], or innovative tribo-system designs [25,26], not only going beyond mere buzzwords, but actually resulting in improved energy efficiency, reduced emissions, and an enhanced overall system performance [27].…”

Review of Tribological Failure Analysis and Lubrication Technology Research of Wind Power Bearings

“…The results of this survey provided a basis for further research on machine learning algorithms that can be applied to the measurement of bearing sensors. Zavos et al [ 200 ] developed an ML model based on an ordered regression algorithm in order to predict the coating application and lubricant selection for bearing components. A tribological analysis model was developed with various lubricants and coatings for bearings as parameters and the models with different regression methods accurately predicted viscous and boundary friction of the bearings.…”