Dragan Milčić (Author), Amir Alsammarraie (Author), Miloš Madić (Author), Vladislav Krstić (Author), Miodrag Milčić (Author)

Abstract

This paper explores the influence of the frequency of shaft sleeve rotation and radial load on a journal bearing made of tin-babbitt alloy (Tegotenax V840) under hydrodynamic lubrication conditions. An experimental test of the frictional behaviour of a radial plain bearing was performed on an originally developed device for testing rotating elements: radial and plain bearings. Using the back-propagation neural network, based on experimental data, artificial neural network models were developed to predict the dependence of the friction coefficient and bearing temperature in relation to the radial load and speed. Using experimental data of the measured friction coefficient with which the artificial neural network was trained, well-trained networks with a mean absolute percentage error on training and testing of 0.0054 % and 0.0085 %, respectively, were obtained. Thus, a well-trained neural network model can predict the friction coefficient depending on the radial load and the speed.

Keywords

artificial neural network;hydrodynamic journal bearing;babbitt metal tin-based alloy;friction coefficient;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FS - Faculty of Mechanical Engineering
UDC: 621.8:681.5
COBISS: 82464515 Link will open in a new window
ISSN: 0039-2480
Views: 129
Downloads: 55
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Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: Slovenian
Secondary title: Napovedovanje količnika trenja pri hidrodinamičnem radialnem drsnem ležaju z uporabo umetnih nevronskih mrež
Secondary keywords: umetna nevronska mreža;hidrodinamični radialni drsni ležaj;količnik trenja;zlitina babbitt-kositer;
Type (COBISS): Article
Pages: str. 411-420
Volume: ǂVol. ǂ67
Issue: ǂno. ǂ9
Chronology: Sep. 2021
DOI: 10.5545/sv-jme.2021.7230
ID: 13763226