Ball screw is a mechanical device widely used in mechanical field. The reverse clearance of ball screw will reduce its precision. In order to eliminate the reverse clearance, it is necessary to apply preload to the ball screw. It is very difficult to measure the preload in real time, and the data are large and time-consuming. By using machine learning method to predict and supervise preload, the changing trend of working condition of ball screw can be evaluated in advance, and the working precision of screw is controlled, which has important engineering significance. In this article, the relationship between the preload and the friction torque is obtained through theoretical derivation and experimental verification. Then, the support vector machine is used as a tool to model the friction torque of ball screw with the parameters of material, lubrication, and revolution, and predict the value and trend of preload to complete the supervision and prediction of the preload of the ball screw. By comparing the experimental results, it is proved that the support vector machine is feasible in predicting and supervising the attenuation of the preload of ball screw.
Prediction of preload attenuation of ball screw based on support vector machine
