Prediction of Friction Torque and Temperature on Axial Angular Contact Ball Bearings for Threaded Spindle Using Artificial Neural Network

Purpose As it is already known, modern industry is based on CNC machines, robots, actuators etc. Most of this equipment has in its structure threaded spindles. Installation of the threaded spindles is the most demanding task in mechanical engineering. Methods The bearings which are designed for this purpose have to meet many requirements regarding bigger number of revolutions, significant axial load, work accuracy, thermal stability etc. Results In order to ensure thermal stability of this kind of bearings, it is necessary to predict values of the main operating parameters of the bearing: total friction torque as well as temperature. This paper presents prediction of the total friction torque and temperature by axial angular contact ball bearings designed for threaded spindles using ANN (Artificial Neural Network). Conclusion As the main validating parameter was selected Mean Absolute Percentage Error (MAPE) which was in range 0,18- 1,32% for all ANNs.