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  • Predictions of friction coefficient in hydrodynamic journal bearing using artificial neural networks
    Milčić, Dragan ...
    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.
    Vir: Strojniški vestnik = Journal of mechanical engineering. - ISSN 0039-2480 (Vol. 67, no. 9, Sep. 2021, str. 411-420)
    Vrsta gradiva - članek, sestavni del
    Leto - 2021
    Jezik - angleški
    COBISS.SI-ID - 82464515

    Povezava(-e):

    https://www.sv-jme.eu/sl/article/predictions-of-friction-coefficient-in-hydrodynamic-journal-bearing-using-artificial-neural-networks/
    Repozitorij Univerze v Ljubljani – RUL
    Digitalna knjižnica Slovenije - dLib.si
    DOI

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