Predictions of friction coefficient in hydrodynamic journal bearing using artificial neural networks - Milčić, Dragan (Strojniški vestnik, 2021)
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; babbitt metal tin-based alloy; friction coefficient; hidrodinamični radialni drsni ležaj; hydrodynamic journal bearing; količnik trenja; umetna nevronska mreža; zlitina babbitt-kositer