Mechanism analysis and accuracy prediction for kinematic errors of machine tool

Kinematic errors of the translational and rotary axes significantly affect the performance of machine tools. Numerous researchers have developed approaches to kinematic errors measurement and identification. Nevertheless, few attention has been paid to predictive methodology. In this paper, a general and efficient prediction method is proposed based on the mechanism and characteristics of kinematic errors. Initially, machine error is systematically categorized following the causality principle to understand the error mechanism at fundamental level. Subsequently, the generation mechanism of kinematic error is analyzed, and the accuracy prediction model is established using a superposition of trigonometric and linear functions. Then, a case study of a precision horizontal grinding machine is developed, and the kinematic errors are measured by a laser interferometer and an inductance micrometer. Findings reveal that the prediction curves of kinematic errors are consistent with the measurement results, with the R-square ranging from 0.769 to 0.902. The proposed kinematic errors prediction model has the potential to apply to machine tool design to enhance the machining accuracy.