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

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