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.
Highlights
Hydrodynamic bearings support a rotating shaft with its associated loads, through a pressure field developed within the lubricant that separates the solid surfaces
The results indicated that the friction coefficient decreased as the load increased to 30 N and remained steady at high loading but decreased with an increase in sliding speed
In order to establish a mathematical relationship between the dependence of the experimental parameters of the journal bearing test in the conditions of hydrodynamic testing, two artificial neural network (ANN) models were developed
Summary
Hydrodynamic bearings support a rotating shaft with its associated loads, through a pressure field developed within the lubricant that separates the solid surfaces. The coefficient of friction is significantly influenced by: normal load, geometry, relative surface motion, sliding velocity, the surface roughness of the rubbing surfaces, the type of material, system rigidity, temperature, stick-slip, relative humidity, lubrication and vibration. Among these factors, normal load and sliding velocity are the two major factors that play significant roles in the variation of the friction coefficient [1] to [4]. Search results have proved that WM-2 and WM-5 alloys can be used in dry sliding conditions, it is shown that the performance of WM-5 under heavy service conditions is better than WM-2 due to its alloying elements
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