Prediction model for pre-travel error in on-machine measurement of spherical surfaces in joint bearings using a lever gauge

Abstract

On-machine measurement is a critical technology that enhances manufacturing precision and efficiency in the production of spherical surfaces for joint bearings. However, the accuracy of fitting reference ball center coordinates for short arc measurements utilizing a lever gauge remains low. The simple calculation of the distance between reference ball center and the calibration points does not suffice for precise identification of pre-travel error. This limitation significantly compromises the measurement accuracy of spherical surfaces. Therefore, this paper proposes a novel method for establishing and identifying a pre-travel error prediction model specifically for on-machine measurements conducted with a lever gauge. Initially, the mechanical structure of the lever gauge and the principles governing on-machine measurement of spherical surface was analyzed. This analysis focused on mechanism of pre-travel error, considering factors such as motion, contact force, and probe pose. Subsequently, a strategy for selecting calibration points was developed, tailored to the measurement requirements spherical surfaces in joint bearings. The parameters of the pre-travel error prediction model were determined using measurement data from reference ball calibration points, which were collected by the lever gauge at various pre-travel distances. The efficacy of the pre-travel error compensation method was ultimately verified through on-machine measurements of spherical surfaces on reference balls and plain bearings. The results indicate that, pre-travel error compensation significantly reduces the measurement error for spherical surfaces on reference balls to less than 0.7 μm, thereby improving the measurement accuracy by 57.1 %. For spherical surfaces in joint bearings, the measurement error after compensation is decreased to less than 1.4 μm, resulting in an improvement in measurement accuracy of 53.3 %. The compensation results show that the proposed prediction model for pre-travel error can improve the on-machine measurement accuracy considerably.