A multi-scale spatial–temporal interaction fusion network for digital twin-based thermal error compensation in precision machine tools

The machining accuracy of precision machine tools (PMTs) directly determines the quality of high-accuracy and complex components and thermal error (TE) significantly affects the machining accuracy of PMTs. The TE compensation is an effective way to reduce its effect and improve the machining accuracy. But the real-time performance of the TE compensation system and the prediction performance and robustness of the TE model are weak. In this study, a multi-scale spatial–temporal interaction fusion network (MSIFN) is designed and embedded into a digital twin framework for TE compensation to address the above issues. The efficient multi-scale squeeze-and-excitation network (EMSENet), spatial graph convolutional network (SGCN), and gated recurrent unit-temporal convolutional network (GRU-TCN) modules are designed for the MSIFN model to comprehensively capture and integrate spatial–temporal behaviors of thermal data. The EMSENet module is designed to emphasize critical features and suppress noise through multi-scale and channel attention mechanisms. The SGCN is able to realize accurate spatial relationship modeling, while the GRU-TCN is used to fuse spatial and temporal features, enhancing predictive accuracy and robustness. A lightweight digital twin-based TE compensation system is proposed, integrating the MSIFN model for real-time prediction and dynamic updates. Experimental results demonstrate the superior predictive performance of MSIFN, achieving a 38.9 % reduction in root mean square error and enhanced robustness compared to baseline models. Moreover, the total executing time of the TE compensation system based on the perception control-edge-cloud framework is reduced by 50.2 % compared with that of the TE compensation system based on the mist-cloud framework and that the reduction rates of the machining error is in the range of [61.5 %, 83.33 %] and [82.2 %, 83.3 %] at the initial and thermal states. This study provides a robust solution for improving machining accuracy in complex industrial environments.