Abstract

Regenerative chatter is an important factor affecting the efficiency and quality of robot milling of large and complex curved parts, especially when the weak rigid posture is forced to be selected under geometric interference. And the regenerative chatter in robotic milling is not only related to the tool-spindle mode but also affected by the tool point low-frequency vibration caused by robot structure mode. At some specific spindle speed, the tool point low-frequency vibration interrupts the occurrence process of chatter, increases the critical cutting depth, and generates a stability boundary improvement domain (SBID). This phenomenon makes it possible to avoid the adverse effects of the weak rigidity of the robot structure, and ensure and even improve machining efficiency. In order to reveal the mechanism of low-frequency vibration improving the stability boundary, a tool-workpiece engagement state considering the tool point low-frequency vibration caused by robot structure mode is analyzed, and radial and tangential tool-workpiece separation models, time-varying process damping model, and axial cutting depth modulation model are established to describe the mechanism of tool-workpiece engagement state. And then the time delay coefficient dependent on the cutting state is proposed. Finally, the stability prediction model considering the tool point low-frequency vibration caused by robot structure mode is established and verified by experiments with different postures. Through simulation and experiment with different postures, a set of evaluation indexes is proposed to evaluate the affection of the low-frequency vibration, and it is found that different robotic postures will lead to different SBIDs, thus proving the effectiveness of posture optimization on stability improvement. The results show that the proposed robotic milling stability model can effectively characterize the effect of low-frequency vibration on the stability boundary, and provide a theoretical basis for accurate positioning of SBID and efficient robotic milling.

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