Pose optimization in robotic machining using static and dynamic stiffness models |
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Affiliation: | 1. Ph.D. Program in Robotics, Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332, United States;2. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332, United States;1. Integrated Manufacturing Research and Application Center, Faculty of Engineering and Natrual Sciences, Sabanci University, Istanbul, 34956, Turkey;1. Hamburg University of Technology, Institute of Production Management and Technology, Denickestrasse 15, Hamburg 21073, Germany;2. Manufacturing Automation Laboratory, The University of British Columbia, Department of Mechanical Engineering, Vancouver, BC V6T 124, Canada |
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Abstract: | Industrial robots are typically not used for milling of hard materials due to their low stiffness compared to traditional machine tools. Due to milling being a five degree of freedom (dof) operation, a typical six dof serial manipulator introduces a redundant degree of freedom in the robot pose. This redundancy can be exploited to optimize the pose of the robot during milling to minimize force-induced deflections at the end-effector. Stiffness modeling and optimization techniques for industrial robots utilizing both static (no mass and damping terms) and dynamic (mass and damping terms included) models exist. This paper presents a comparative study of robot pose optimization using static and dynamic stiffness models for different cutting scenarios. Milling experiments show that while a dynamic model-based robot pose optimization yields significant improvement over a static model-based optimization for cutting conditions where the time varying cutting forces approach the robot's natural frequencies, a static model-based optimization is sufficient when the frequency content of the cutting forces are not close to the robot's natural frequencies. |
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