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Selection of Lubricants and Coatings for Engine Components Using Machine...
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Selection of Lubricants and Coatings for Engine Components Using Machine Learning

A special issue of Coatings (ISSN 2079-6412). This special issue belongs to the section "Surface Characterization, Deposition and Modification".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 8234

Special Issue Editors

Dr. Pantelis G. Nikolakopoulos

E-Mail Website
Guest Editor
Machine Design Laboratory, Department of Mechanical and Aeronautics Engineering, University of Patras, 26504 Patras, Greece
Interests: friction; wear; lubrication; machine elements; coatings; magnetorheologila fluids; journal bearings; piston rings
Dr. Anastasios Zavos

E-Mail Website
Guest Editor
Machine Design Laboratory, Department of Mechanical and Aeronautics Engineering, University of Patras, 26504 Patras, Greece
Interests: modeling; CFD simulation; finite element method; structural analysis; lubrication; coatings; wear; surface characterization; texturing; piston rings

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Coatings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

19 pages, 6118 KiB  
Article
Experimental Characterization of a Foil Journal Bearing Structure with an Anti-Friction Polymer Coating
by Grzegorz Żywica, Paweł Bagiński, Jakub Roemer, Paweł Zdziebko, Adam Martowicz and Tomasz Zygmunt Kaczmarczyk
Coatings 2022, 12(9), 1252; https://doi.org/10.3390/coatings12091252 - 26 Aug 2022
Cited by 2 | Viewed by 2687
Abstract
The development of highly efficient and environmentally friendly machines requires the use of new technologies that are created using innovative design solutions and new materials. This also applies to various types of propulsion units, such as gas microturbines or combustion engines. Although these [...] Read more.
The development of highly efficient and environmentally friendly machines requires the use of new technologies that are created using innovative design solutions and new materials. This also applies to various types of propulsion units, such as gas microturbines or combustion engines. Although these machines have been known for many years, by using new components, it is still possible to improve their performance. This article presents an experimental study conducted on a gas foil bearing using a polymer coating as an anti-friction material. These types of bearings allow for a reduction in friction losses and are not lubricated with conventional lubricants. The dynamic characteristics of the foil bearing structure were determined, which are essential in terms of both rotor dynamics and the entire propulsion system. The research was carried out over a wide range of frequencies, with different loads acting in different directions. Hysteresis loops and vibration orbits were determined. The authors showed that displacements perpendicular to the load in some cases may be relatively large and should not be ignored. The results obtained during the tests can be used to validate numerical models of such bearings, optimize their design and select the structural and anti-friction materials. Full article
(This article belongs to the Special Issue Selection of Lubricants and Coatings for Engine Components Using Machine Learning)
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24 pages, 3301 KiB  
Article
Optimum Selection of Coated Piston Rings and Thrust Bearings in Mixed Lubrication for Different Lubricants Using Machine Learning
by Anastasios Zavos, Konstantinos P. Katsaros and Pantelis G. Nikolakopoulos
Coatings 2022, 12(5), 704; https://doi.org/10.3390/coatings12050704 - 20 May 2022
Cited by 5 | Viewed by 3177
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Selection of Lubricants and Coatings for Engine Components Using Machine Learning)
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17 pages, 7218 KiB  
Article
Tribological Behavior of Ni-Based Coatings Deposited via Spray and Fuse Technique
by Jiménez Hernando, Olaya Jhon Jairo and Alfonso José Edgar
Coatings 2020, 10(11), 1080; https://doi.org/10.3390/coatings10111080 - 10 Nov 2020
Viewed by 2007
Abstract
The tribological behavior of Ni-based coatings was analyzed. The coatings were deposited on grey cast iron substrates in a spray and fuse process using Superjet Eutalloy deposition equipment, varying the oxygen flow conditions in the flame. By means of the X-ray diffraction (XRD) [...] Read more.
The tribological behavior of Ni-based coatings was analyzed. The coatings were deposited on grey cast iron substrates in a spray and fuse process using Superjet Eutalloy deposition equipment, varying the oxygen flow conditions in the flame. By means of the X-ray diffraction (XRD) technique, the crystal structure of the coatings was determined. The XRD patterns show the crystalline phases with principal reflections for Ni in the planes (111) and (222). Crystalline properties such as the orientation coefficient, crystallite size, and macrostrain showed the relationship with tribological and mechanical properties such as the dry wear rate and the microhardness. The microhardness was analyzed on the surface and on cross sections of the coatings by means of a Knoop microhardness tester. The topography and the morphological characteristics of the coatings and the tribo-surfaces were exanimated using scanning electron microscopy (SEM) and confocal microscopy, while the chemical composition was measured by means of energy-dispersive X-ray spectroscopy (EDS). The tribological behavior of the coatings was examined via the scratch cohesion–adhesion test, using cross sections of the coatings. Furthermore, adhesion and abrasion wear tests were carried out, using the pin-on-disk method, under the ASTM G99 standard and the ASTM G65 standard, respectively. The wear rate of the coatings showed a strong relation to the porosity in the metal matrix, which was previously determined via electrochemical characterization techniques. Full article
(This article belongs to the Special Issue Selection of Lubricants and Coatings for Engine Components Using Machine Learning)
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