Journal
ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
Volume 75, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.rcim.2021.102307
Keywords
Stability prediction; Robotic milling; Random forest; Multi-mode dynamics
Funding
- National Natural Science Foundation of China [91948203, 51525501]
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This paper investigates the stability prediction of robotic milling for non-metallic materials and proposes an effective prediction model considering variable modal properties. The proposed method is validated through cutting experiments on an industrial robot.
Industrial robots are promising to be used in the light-load machining, especially of non-metallic materials. Nevertheless, chatter is also prone to occur in such operating conditions due to the low stiffness of robot, which deteriorates the surface finish and machining efficiency. It is essential to study the dynamics of robotic ball-end milling process for predicting and suppressing chatter. In this paper, an effective stability prediction model considering variable modal properties is constructed for milling non-metallic curved parts by an industrial robot with high-speed spindle. To obtain the pose-dependent modal parameters of the robot, a random forest based multimode prediction method is proposed, which performs well for stability limit prediction. Not only the diagonal terms but also the cross terms of the modal matrix are predicted. Subsequently, a transformation matrix is constructed to transfer the frequency response functions (FRFs) that fitted by above predicted parameters into the direction of the instantaneous tool orientation. Combined with the pose-independent tool mode linearly, the integrated modal matrix of the tool tip is obtained. Finally, a multiple degree of freedom dynamic equation simultaneously considering the robot mode and tool mode is established and then solved by an extended second order semi-discretization method (2nd SDM) for stability prediction. Indeed, the effects of robot pose, cross coupling and tool orientation on stability are deeply analyzed, which provide a meaningful guidance for robotic milling operations. The effectiveness of the proposed method is validated by a series of cutting experiments on an industrial robot.
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