Research on the thermal characteristics of motorized spindle under magnetic–thermal coupling based on digital twin

In operation, traditional mechanistic models fail to iteratively update with the motorized spindle, leading to inaccuracies in dynamic data like the motorized spindle's internal magnetic and thermal fields. To address the issue, we propose a data-driven, magnetic–thermal bidirectional coupling twin model based on digital twin technology. This model, considering temperature effects on the motorized spindle motor's materials, enables dynamic updates through a twin database and service platform. Comparative analysis shows minor temperature calculation differences between this twin model and traditional mechanistic models at speeds below 9000 r/min. As the speed reaches 9000 r/min and the motorized spindle's efficiency peaks, the disparity between the two models grows. Experimental data show that the twin model outperforms the traditional model in temperature accuracy at the motorized spindle's test points, achieving up to a 4.57% improvement in front bearing temperature calculations. The proposed motorized spindle twin model demonstrates higher accuracy, providing significant assistance in the dynamic process simulation and operational monitoring of the motorized spindle.