Co-simulation-based digital twin for thermal characteristics of motorized spindle

To improve the accuracy of thermal characteristics analysis of motorized spindle, an online correction model of thermal boundary conditions is proposed based on BP neural network (BPNN), the experimental data and simulation results are used to construct the BPNN model to correct the thermal boundary conditions of motorized spindle. Based on the co-simulation of Ansys, Matlab, and LabVIEW, a digital twin system for thermal characteristics is built to precisely predict the temperature field and thermal deformation of a motorized spindle under varied operating conditions. The experimental results show that the prediction accuracy of temperature field is greater than 98%, and the prediction accuracy of thermal deformation is greater than 96%, which effectively improves the simulation accuracy and robustness of thermal characteristics, and provides the foundation for the error compensation and thermal optimization design.