Selection of Lubricants and Coatings for Engine Components Using Machine Learning

Dear Colleagues,

Currently, an environmentally friendly approach is considered to be of the highest importance in the field of internal combustion engines (ICEs). Over the last few decades, ICEs have become a critical topic in the automotive industry due to the internal sub-systems associated with fuel and oil consumption. Machine components such as journal bearings, piston rings, gears, crankshaft bearings, cams, brakes, clutches, and floating ring bearings are crucial to an ICE’s operation. Optimization of each component will result in an increase in power efficiency and a reduction in specific fuel consumption, emissions, and environmental footprint, contributing to the more environmentally friendly operation of ICEs. Simultaneously, the rise in computing power and massive network speeds has led to rapid changes in technology and industry. The 4th industrial evolution or Industry 4.0 conceptualizes a wide range of applications, such as Artificial Intelligence (AI) and Machine Learning (ML), which have been already applied in many technological areas including mechanical engineering. Machine Learning (ML) methods have been used in engineering for the solution of complex, non-linear, and multi-dimensional problems that depend on a large amount of data.

This Special Issue will examine the effect of coating materials on the global fuel economy and emissions of ICEs through multi-scale and multi-physics modeling using ML techniques. Advances in this field will be based on the development of appropriate numerical models, surface treatments, and fundamental experimental processes. Potential topics include but are not limited to:

• Multi-scale and multi-physics modeling of ring and bearing tribology;
• Topographical characterization in boundary and mixed lubrication;
• Identification of the roles of coatings and lubricants using Machine Learning and experimental techniques;
• Surface characterization under extreme contact conditions;
• Maching Learning in lubricated contacts.

Dr. Pantelis G. Nikolakopoulos
Dr. Anastasios Zavos
Guest Editors

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• engine tribology
• engine lubricant additives
• machine learning
• piston rings
• journal bearings
• thrust bearings