Research on Visualization and Thermal Characteristics Analysis of Spindle Driven by Digital Twins

The thermal characteristics of the spindle are crucial to its operational performance. Current research methods still need improvement in terms of accuracy and real-time capabilities. Integrating digital twin technology with a mechanism-data hybrid-driven model offers a promising solution to this issue. However, research on digital twin systems for spindle thermal characteristics is still in its exploratory stages, with real-time interaction and visualization functions being insufficiently developed. In this context, this paper constructs a spindle digital twin system that deploys a thermal characteristic analysis model based on a corrected thermal network. The system is built on the five-dimensional digital twin theory, enabling the model to receive real-time data from the physical spindle and continuously adjust its parameters, thereby improving the accuracy of spindle thermal characteristic analysis. Additionally, the digital twin system integrates visualization and thermal characteristic mapping modules, achieving virtual-real interaction and real-time mapping of spindle thermal characteristics, laying the foundation for further spindle performance optimization and intelligent maintenance.