In some cases, the static model updating requires more precise results to satisfy the requirement of simulating the structural response. The corresponding optimization algorithm not only should have a stronger capability to find the global optimum, but also should achieve an optimal result with high accuracy. The intelligent algorithm and direct searching method have the advantages of global and local optimization, respectively. In the present study, a hybrid strategy based on the intelligent algorithm and direct method are proposed, and a typical test function is initially utilized to test its accuracy. The re-entry module is taken as the case study, whose thermal deformation is used to construct the objective function for optimization, and precise results are achieved. Moreover, the high-accuracy results in different objective functions are obtained. In order to investigate the prediction capability of the proposed method, the displacements in another structural direction are extracted and show high accuracy, which demonstrates the feasibility and efficiency of the proposed approach. The results show that the hybrid algorithm can achieve a better result than the single algorithm in the static model updating.

It is easy to cause thermal damage to the bone tissue when the surgical robot performs skull drilling to remove bone flaps, due to the large diameter of the drill bit, the large heat-generating area, and the long drilling time. Therefore, in order to reduce the thermal damage during the robot-assisted skull drilling process, the relationship between the drilling parameters and the drilling temperature during the skull drilling was studied in this paper. Firstly, a dynamic numerical simulation model of the skull drilling process was established by ABAQUS, and a temperature simulation plan for skull drilling was designed based on the Box–Behnken method. Then according to the simulation results, a quadratic regression model of drill diameter, feed rate, drill speed, and drilling temperature was established by using the multiple regression method. By analyzing the regression model, the influence of drilling parameters on the drilling temperature was clarified. Finally, the bone drilling experiment was carried out, and the error percentage was lower than 10.5% through the experiment to verify the reliability of the conclusion, and a safety strategy was proposed to ensure the safety of the surgical drilling process based on this experiment.