Industrial robots offer significant advantages in milling applications, due to their expansive workspace and high operational flexibility. However, their serial structure inherently exhibits significantly lower Cartesian stiffness than machine tools, making them highly prone to deformation and chatter during the machining process. While pose optimization is a standard approach to mitigate these issues, existing methods primarily focus on enhancing global stiffness within the robot’s workspace, often overlooking the local stiffness consistency required on the specific machining plane. This discrepancy leads to uneven machining accuracy and reduced surface quality. To address this challenge, this paper proposes a layered optimization method that bridges global workspace planning with local process refinement. First, a stiffness model is established based on the virtual joint method. A global optimization strategy, incorporating stiffness ellipsoid volume and kinematic constraints, is then designed to determine the optimal machining pose and workpiece placement within the workspace. Subsequently, for the local machining plane, a composite index combining the cross-sectional stiffness ellipse area and modal coupling chatter is constructed. Based on this index, a local optimization algorithm adjusts the feed direction and redundant angle to synergistically enhance stiffness and dynamic stability. Comparative experiments on an ABB IRB 4600 robot demonstrate that the proposed method significantly improves machining quality. The results show that the maximum profile error of the milled surface is reduced from 22 . 7 μ m to 5 . 9 μ m , verifying its effectiveness in enhancing machining precision.
Gao et al. (Fri,) studied this question.
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