The aerostatic spindle is a key component of ultra-precision machine tools, and its error motion is crucial to machining accuracy and reliability. Spindle error motion is unavoidable, and its online monitoring and prediction are quite important. Currently, there are relatively few studies on the online monitoring and prediction methods for the aerostatic spindle, and the level of intelligence is relatively low. To address this problem, an error motion monitoring system based on digital twin (DT) technology was established for the aerostatic spindle. A spindle error motion prediction method based on a mechanism and data fusion model (MDFM) was proposed. Additionally, a highly available and interactive aerostatic spindle DT service platform was developed. Experimental results have verified the good performance of this platform. The platform facilitates interaction between the physical and virtual entities of the aerostatic spindle, enabling three-dimensional visualization, monitoring, prediction, and simulation of spindle error motion, and shows good potential for engineering applications.
Chen et al. (Thu,) studied this question.