Chatter has always been a key problem restricting the improvement of robotic milling quality and efficiency. To avoid chatter, it is necessary to determine what is the dominant chatter mechanism (mode coupling or regenerative) of the robot milling system. Therefore, this paper focus on the dominant chatter mechanism in high-load (600kg) robot milling. The modal test results show that the dynamic flexibility of spindle-tool structure mode in high-load robot is significantly higher than that of the body structure mode, which is significantly different from the low-load robot in other studies. The mode coupling chatter stability prediction models are established based on eigenvalue method and zeroth order approximation, and the predicted stability boundaries are compared with the experimental results. The results show that only high-frequency chatter exists in the high speed region (1000-8000rpm), and no low frequency chatter occurs. The low-frequency chatter around the robot body mode is found in the low-speed region (400-1000rpm), but the mode coupling chatter theory could not explain the chatter varies periodically with the spindle speed. However, the stability boundary predicted by the regenerative chatter theory also changes periodically with the spindle speed. This indicates that the milling chatter dominant mechanism of high load robot is regenerative chatter. This study analyzes the milling chatter dominant mechanism of high-load robot through theoretical and experimental verification, which can provide theoretical support for high-load robot milling chatter control.

The change of tool tip frequency response caused by the posture dependence of robot dynamic is one of the key problems that make it difficult to accurately predict the milling stability of robot. In this paper, a tool tip frequency response prediction method considering the interface stiffness characteristics of spindle-tool system is proposed for stability prediction of robotic milling under the condition of posture variation. Firstly, the interface stiffness models of spindle-toolholder, toolholder-spring clip and spring clip-tool are established based on Yoshimura's unit area method. Then, The dynamics model for the robot body and the interface stiffness models for spindle-tool system are imported into the finite element analysis model of the spindle system, so that the prediction of the tool tip frequency response is realized by harmonic response analysis. Compared with the experimental results, the maximum error of the natural frequency was not more than 2 %, and the maximum error of the amplitude was not more than 12%. Finally, the 2 DOF robot milling stability prediction model is established. Then the robot milling chatter is predicted considering redundant degrees of freedom from the perspective of regenerative chatter prediction theory, and the accuracy of prediction results is verified by milling experiment.

Undesirable self-excited chatter has always been a typical issue restricting the improvement of robotic milling quality and efficiency. Sensitive chatter identification based on processing signals can prompt operators to adjust the machining process and prevent chatter damage. Compared with the traditional machine tool, the uncertain multiple chatter frequency bands and the band-moving of the chatter frequency in robotic milling process make it more challenging to extract chatter information. This paper proposes a novel method of chatter identification using optimized variational mode decomposition (OVMD) with multi-band information fusion and compression technology (MT). During the robotic milling process, the number of decomposed modes k and the penalty coefficient α are optimized based on the dominant component of frequency scope partition and fitness of the mode center frequency. Moreover, the mayfly optimization algorithm (MA) is employed to obtain the global optimal parameter selection. In order to conquer information collection about the uncertain multiple chatter frequency bands and the band-moving of the chatter frequency in robotic milling process, MT is presented to reduce computation and extract signal characteristics. Finally, the cross entropy of the image (CEI) is proposed as the final chatter indicator to identify the chatter occurrence. The robotic milling experiments are carried out to verify the proposed method, and the results show that it can distinguish the robotic milling condition by extracting the uncertain multiple chatter frequency bands and overcome the band-moving of the chatter frequency in robotic milling process.

Undesirable chatter is one of the key problems that restrict the improvement of robot milling quality and efficiency. The prediction of chatter stability, which is used to guide the selection of process parameters, is an effective method to avoid chatter in robot milling. Due to the weak stiffness of the robot, deformation caused by milling forces becomes an unavoidable problem, which will change the tool–workpiece contact area and affect the stability prediction. However, it is often simplified and neglected. In this paper, a multipoint contact dynamic model of robot milling is established, which considers the influence of force-induced deformation on the regenerative effect and process damping. The tool–workpiece contact area is discretized into a finite number of nodes along the axial direction so that the force and deformation at each node can be calculated separately. The different contact forms of the tool–workpiece under different process parameters are discussed in different cases, and the interaction process between cutting force and force-induced deformation is analyzed in detail. An iterative strategy is used to calculate the deformation of each node and the result of the tool–workpiece contact boundary. Finally, chatter stability of robot milling is predicted by a fully discrete method. Robot milling experiments were carried out to verify the predicted results. The results show that force-induced deformation is an important factor improving the stability prediction accuracy of robot milling, and a more accurate prediction result can be obtained by simultaneously considering force-induced deformation and process damping.