Predictions of friction coefficient in hydrodynamic journal bearing using artificial neural networks

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

Povzetek

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.

Ključne besede

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

Podatki

Jezik:	Angleški jezik
Leto izida:	2021
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UL FS - Fakulteta za strojništvo
UDK:	621.8:681.5
COBISS:	82464515
ISSN:	0039-2480
Št. ogledov:	129
Št. prenosov:	55
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Slovenski jezik
Sekundarni naslov:	Napovedovanje količnika trenja pri hidrodinamičnem radialnem drsnem ležaju z uporabo umetnih nevronskih mrež
Sekundarne ključne besede:	umetna nevronska mreža;hidrodinamični radialni drsni ležaj;količnik trenja;zlitina babbitt-kositer;
Vrsta dela (COBISS):	Članek v reviji
Strani:	str. 411-420
Letnik:	ǂVol. ǂ67
Zvezek:	ǂno. ǂ9
Čas izdaje:	Sep. 2021
DOI:	10.5545/sv-jme.2021.7230
ID:	13763226