20.500.12556/RUL-132473 Predictions of friction coefficient in hydrodynamic journal bearing using artificial neural networks Napovedovanje količnika trenja pri hidrodinamičnem radialnem drsnem ležaju z uporabo umetnih nevronskih mrež 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. artificial neural network hydrodynamic journal bearing babbitt metal tin-based alloy friction coefficient umetna nevronska mreža hidrodinamični radialni drsni ležaj količnik trenja zlitina babbitt-kositer true false true Angleški jezik Slovenski jezik Članek v reviji 2021-10-27 08:28:53 2021-10-27 08:28:58 2022-09-12 04:12:33 0000-00-00 00:00:00 2021 0 0 Str. 411-420 no. 9 Vol. 67 Sep. 2021 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 621.8:681.5 0039-2480 10.5545/sv-jme.2021.7230 82464515 RAZ_Milcic_Dragan_2021.pdf RAZ_Milcic_Dragan_2021.pdf 1 0C44B1E21FDFE411E337837434812014 99c00ade83cc833dcc2c715422df29d6bd92e2d95ee0ed7491f5e501a9b5d424 43efed97-36ef-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=150117 https://www.sv-jme.eu/sl/article/predictions-of-friction-coefficient-in-hydrodynamic-journal-bearing-using-artificial-neural-networks/ 1 243be1d1-36ef-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=150116 Fakulteta za strojništvo Strojniški vestnik 0 0 0