Positioning accuracy degradation and lifetime prediction of the ball screw considering time-varying working conditions and feed modes

Abstract

Ball screw mechanisms (BSMs) are used as accuracy transmission components in a wide range of industries and are characterized by their high accuracy. More specifically, the positioning accuracy of BSM has a significant effect on the accuracy of machine tool. Based on the macro-micro multiscale method, an exponential prediction model for the BSM positioning accuracy was developed considering time-varying working conditions (load and rotational speed) and feed modes. Since the accuracy degradation is mainly caused by wear, a microscopic approach was proposed to describe the positioning accuracy retention and the microscopic wear process was investigated. The sliding contact of the asperities between the ball and raceway was analyzed, and the microscopic wear behavior of the asperities was determined. Considering the time-varying working conditions, the BSM positioning accuracy characteristics were obtained under the normal feed mode by conducting suitable tests. The exponential wear model used the wear index to describe the wear status based on the positioning accuracy measurement. The accuracy loss value and the prediction index of positioning accuracy were determined based on an exponential model, and the effective lifetime of the BSM was predicted. Finally, the exponential prediction model was used in negative/positive skew feed distribution, and the effective lifetime determined.