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Predictions of friction coefficient in hydrodynamic journal bearing using artificial neural networks
ID Milčić, Dragan (Author), ID Alsammarraie, Amir (Author), ID Madić, Miloš (Author), ID Krstić, Vladislav (Author), ID Milčić, Miodrag (Author)

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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.

Language:English
Keywords:artificial neural network, hydrodynamic journal bearing, babbitt metal tin-based alloy, friction coefficient
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FS - Faculty of Mechanical Engineering
Publication status:Published
Publication version:Version of Record
Year:2021
Number of pages:Str. 411-420
Numbering:Vol. 67, no. 9
PID:20.500.12556/RUL-132473 This link opens in a new window
UDC:621.8:681.5
ISSN on article:0039-2480
DOI:10.5545/sv-jme.2021.7230 This link opens in a new window
COBISS.SI-ID:82464515 This link opens in a new window
Publication date in RUL:27.10.2021
Views:521
Downloads:138
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Record is a part of a journal

Title:Strojniški vestnik
Shortened title:Stroj. vestn.
Publisher:Zveza strojnih inženirjev in tehnikov Slovenije [etc.], = Association of Mechanical Engineers and Technicians of Slovenia [etc.
ISSN:0039-2480
COBISS.SI-ID:762116 This link opens in a new window

Secondary language

Language:Slovenian
Title:Napovedovanje količnika trenja pri hidrodinamičnem radialnem drsnem ležaju z uporabo umetnih nevronskih mrež
Keywords:umetna nevronska mreža, hidrodinamični radialni drsni ležaj, količnik trenja, zlitina babbitt-kositer

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