An incremental self-excitation method for effectively identifying low-frequency frequency response function of milling robots

Robotic machining efficiency and accuracy are limited by milling vibration and chatter. Robot dynamic characteristics are strongly dependent on the poses; therefore, acquiring the robot dynamic characteristics in any pose is important for vibration suppression and chatter avoidance in large-range machining. This paper proposes an incremental self-excitation method for effectively identifying low-frequency frequency response functions (FRF) of milling robots. A fully knowable and controllable excitation increment can be achieved by attaching a mass block at the robot end, which overcomes the shortcoming of the traditional self-excitation methods that cannot obtain the dynamic compliance magnitude. With appropriate trajectory programming, this method can be carried out automatically in the poses of interest without manual operations. First, the impulse (moment) of the incremental self-excitation is modeled based on momentum theorem, and the association model of the pulse response increment with the incremental self-excitation is established. For the problem that the FRF calculation process is sensitive to noise, the incremental self-excitation is assumed to be a Gaussian pulse, and its identification method is provided. Then, the dimensionality requirement for identifying the 9-item (direct and cross) FRFs is reduced using the modal directionality of milling robots, and the corresponding FRF calculation method is proposed. The rationality of the required simplifications and assumptions of this method is verified by experiments and calculations. The experimental results in several robot poses show that the proposed method can effectively identify all the direct and cross FRFs in the low-frequency band.