{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T03:58:46Z","timestamp":1777694326099,"version":"3.51.4"},"reference-count":50,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ICA"],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>A hydrodynamic thrust bearing could be forced to operate in mixed lubrication regime under various circumstances. At this state, the tribological characteristics of the bearing could be affected significantly and the developed phenomena would have a severe impact on the performance of the mechanism. Until recently, researchers were modeling the hydrodynamic lubrication problem of the thrust bearings either with analytical or with numerical solutions. The analytical solutions are very simple and do not provide enough accuracy in describing the actual problem. To add to that, following only computational methodologies, can lead to time consuming and complex algorithms that need to be repeated every time the operating conditions change, in order to draw safe conclusions. Recent technological advances, especially on the field of computer science, have provided tools that enhance and accelerate the modeling of thrust bearings\u2019 operation. The aim of this study is to examine the application of Artificial Neural Networks as Machine Learning models, that are trained to predict the coefficient of friction for lubricated pad thrust bearings in mixed lubrication regime. The hydrodynamic analysis of the thrust bearing is performed by solving the Average 2-D Reynolds equation numerically. In order to describe the roughness of the profiles, both the flow factors suggested by N. Patir and H.S. Cheng (1978) and the model of J.A. Greenwood and J. H. Tripp (1970) are taken into consideration. Three lubricants, the SAE 0W30, the SAE 10W40 and the SAE 10W60, are tested and compared for a variety of operating velocities and applied coatings. The numerical analysis results are used as training datasets for the machine learning algorithms. Four different ML methods are applied in this investigation: Artificial Neural Networks (ANNs), Multi- Variable Quadratic Polynomial Regression, Quadratic SVM and Regression Trees. The coefficient of determination, R2 is calculated and used to determine the most accurate ML method for the current study. The results showed that ANNs provide very good accuracy in the prediction of friction coefficient compared to the rest of the ML models discussed.<\/jats:p>","DOI":"10.3233\/ica-240737","type":"journal-article","created":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T10:54:14Z","timestamp":1715684054000},"page":"401-419","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of thrust bearing\u2019s performance in Mixed Lubrication regime"],"prefix":"10.1177","volume":"31","author":[{"given":"Konstantinos P.","family":"Katsaros","sequence":"first","affiliation":[]},{"given":"Pantelis G.","family":"Nikolakopoulos","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/ICA-240737_ref1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1115\/1.2404963","article-title":"Load-responsive hydrodynamic bearing for downhole drilling tools","volume":"129","author":"Kalsi","year":"2007","journal-title":"Journal of Tribology"},{"key":"10.3233\/ICA-240737_ref2","first-page":"221","article-title":"Polycrystalline diamond thrust bearing testing and qualification for application in marine hydrokinetic machines","author":"Lingwall","year":"2012","journal-title":"American Society of Mechanical Engineers, Tribology Division, TRIB"},{"key":"10.3233\/ICA-240737_ref3","doi-asserted-by":"crossref","first-page":"art. no. 106356","DOI":"10.1016\/j.triboint.2020.106356","article-title":"Stability analysis of rubber-supported thrust bearing in a rotor-bearing system used in marine thrusters under disturbing moments","volume":"151","author":"Sun","year":"2020","journal-title":"Tribology International"},{"issue":"4","key":"10.3233\/ICA-240737_ref4","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1007\/s11249-004-8081-1","article-title":"Improving tribological performance of mechanical components by laser surface texturing","volume":"17","author":"Etsion","year":"2004","journal-title":"Tribology Letters"},{"issue":"9\u201310","key":"10.3233\/ICA-240737_ref5","doi-asserted-by":"crossref","first-page":"2106","DOI":"10.1016\/j.wear.2010.12.059","article-title":"Wear-in behaviour of polycrystalline diamond thrust bearings","volume":"271","author":"Knuteson","year":"2011","journal-title":"Wear"},{"issue":"12","key":"10.3233\/ICA-240737_ref6","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1016\/j.triboint.2011.05.008","article-title":"Investigation of tribological behaviors of annular rings with spiral groove","volume":"44","author":"Qi","year":"2011","journal-title":"Tribology International"},{"issue":"2","key":"10.3233\/ICA-240737_ref7","doi-asserted-by":"crossref","first-page":"art. no. 201","DOI":"10.1051\/meca\/2018005","article-title":"Numerical analysis and experimental research on load carrying capacity of water-lubricated tilting-pad thrust bearings","volume":"19","author":"Zhang","year":"2018","journal-title":"Mechanics and Industry"},{"issue":"3","key":"10.3233\/ICA-240737_ref8","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/S0301-679X(02)00145-7","article-title":"Loads carrying capacity map for the surface texture design of SiC thrust bearing sliding in water","volume":"36","author":"Wang","year":"2003","journal-title":"Tribology International"},{"key":"10.3233\/ICA-240737_ref9","first-page":"44","article-title":"Simulation of a thrust bearing in a diesel injection pump under mixed lubrication conditions","author":"Illner","year":"2009","journal-title":"World Tribology Congress"},{"key":"10.3233\/ICA-240737_ref10","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.triboint.2017.12.021","article-title":"Experimental analysis of the hydrodynamic effect during start-up of fixed geometry thrust bearings","volume":"120","author":"Henry","year":"2018","journal-title":"Tribology International"},{"key":"10.3233\/ICA-240737_ref11","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1016\/j.oregeorev.2015.01.001","article-title":"Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines","volume":"71","author":"Rodriguez-Galiano","year":"2015","journal-title":"Ore Geology Reviews"},{"issue":"4","key":"10.3233\/ICA-240737_ref12","first-page":"351","article-title":"Near real-time management of appliances, distributed generation and electric vehicles for demand response participation","volume":"29","author":"Fernandes","year":"2022","journal-title":"Integr. 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