Pose optimization in robotic milling based on surface location error

Abstract

Industrial robots have become a suitable alternative to machine tools due to their great flexibility, low cost, and large working space. However, the deformation and vibration caused by the cutting forces during machining result in poor machining accuracy and surface quality. In order to improve the machining performance of the robot, this paper proposes a posture optimization method for robotic milling with the redundant degree of freedom of the industrial robot. First, modal tests are conducted in the robotic workspace to obtain the parameters of the structural dynamics of the robotic milling system. Then, considering the dynamics model of the system, the optimization model based on surface location error (SLE) is proposed to obtain the optimal robotic posture. Finally, a series of experiments illustrate that pose optimization based on SLE can improve the machining accuracy and surface machining quality.