An Incremental Self-Excitation Method for Effectively Identifying Low-Frequency Frequency Response Function of Milling Robots

Robotic machining efficiency and accuracy are constrained by milling vibrations and chatter. The dynamic characteristics of robots are highly influenced by their poses. Consequently, it is crucial to obtain the robot's dynamic characteristics in any given pose to mitigate vibrations and prevent chatter during large-range machining. This paper proposes an incremental self-excitation method for effectively identifying low-frequency frequency response functions (FRF) of milling robots. By attaching a mass block at the robot's end, a fully knowable and controllable excitation increment can be achieved, overcoming the shortcoming of traditional self-excitation methods in capturing the dynamic compliance magnitude. By employing suitable trajectory programming, this method can be executed automatically in the desired poses without the need for 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. To address the issue of sensitivity to noise in the FRF calculation process, the incremental self-excitation is assumed to be a Gaussian pulse, and its identification method is provided. Subsequently, the dimensionality requirement for identifying the nine-item (direct and cross) FRFs is effectively reduced using the modal directionality of milling robots, and the corresponding FRF calculation method is proposed. The rationality of the simplifications and assumptions employed in this method is validated through 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.