A two-stage dynamic model updating method for the FRF prediction of the robotized mobile machining equipment

Abstract

In the coming decades, robotized mobile machining equipment (RMME) is possible to evolve as a new branch of machine tools due to its exceptional flexibility. The frequency response function (FRF) serves as a theoretical foundation in controlling the vibration deformations that significantly limit the material removal efficiency of RMME. Model updating, aimed at minimizing errors between the theoretical model and the physical prototype, is essential to predict the FRF accurately. However, updating the dynamic model of RMME, characterized by non-mechanical boundary conditions, complex lightweight components, and low-stiffness structures, presents difficulties in computational efficiency and updating posture-dependent parameters. To solve these issues, the prediction error generation mechanism is first analyzed to confirm the error types that need to be eliminated in model updating. A two-stage model updating method that can separately update the robot structure and boundary-related parameters is proposed to rapidly update the dynamic model under various machining tasks. The interface reduction technique that can decrease the model order is introduced to reduce the computational consumption, and an approach to fast update such interface reduction substructures is put forward to avoid the reiterative model reduction during updating. An updating method for posture-dependent parameters based on multi-objective optimization is designed to control the multiple solution issues by generating many feasible solutions, ensuring the prediction effects for non-updated postures. The experimental results indicate that updating structural parameters in stage I and adsorption surface parameters in stage II results in mean error reduction percentages of 58.79 % and 48.30 %, respectively. Additionally, the natural frequencies and the mode shapes can also be predicted by the updated model. Comparative analysis with various controlled groups confirms the advantages of utilizing posture-dependent parameters in prediction accuracy and adopting the two-stage model updating method in efficiency. The proposed method can also be applied to other RMMEs.