Modal parameters prediction for robotic milling based on Gaussian process regression - WAN Min; LI Zhanying; SHEN Chuanjing; WU Xiaojie (Hangkong gongcheng jinzhan)
The acquisition of the frequency response function of the robotic structure and the identification of dynam ic parameters have a significant impact on the prediction of robotic milling，and modal parameters have strong posture-dependence. The finite element method and dynamic model often lose accuracy due to the difficulty in exactly modeling the stiffness and damping properties of robots. To predict the modal parameters quickly and accurately in all robot postures within the machining space，this paper proposes a modal parameter prediction method based on Gaussian process regression. The influence of joint angles and Euler angles of a six degree-of-freedom serial robot on the modal parameters of the robotic milling system is investigated. Based on this，a posture-related modal parameters prediction model is established to characterize the relationship between modal parameters and robot postures through 245 sets of modal percussion experiments in the machining plane. The model can predict the posturerelated modal parameters for all robot postures by a limited number of modal testing experiments. Results show that the proposed method is validated by experiments.