Industrial robots are widely used in manufacturing operations such as drilling, welding, and painting. In recent years, robots have gained significant attraction for aerospace machining (material removal) as well. Compared to conventional CNC machine tools, robots have higher versatility and a lower cost. However, robotic arms suffer from low structural stiffness, which leads to deflection errors under machining loads, as well as vibrations during high speed motions of the arm. To tackle these issues, this thesis presents a systematic framework for the prediction and pre-compensation of positioning errors to improve the accuracy of machining robots.

The kinematic and compliance models of a Stäubli RX-90 industrial robot are first formulated mathematically. In the developed model, it is assumed that the links of the robot are rigid, and therefore all flexibilities originate from the joints. A cutting force model is developed to predict the machining forces exerted on the robot’s end-effector (i.e. milling tool) during 3-axis milling operations. The predicted forces are combined with the compliance model of the robot to determine the structural deflections during machining.

In order to reduce the residual vibrations of the robot in high speed motions, the concept of input shaping is introduced. It is shown that input shaping can distort the reference toolpath, which leads to the deviation of the actual machining path from the desired trajectory, also known as contour error.

Finally, a systematic framework is proposed to predict and compensate for positioning errors in robotic machining. The developed model can determine the contour errors caused by both cutting force-induced deflections and input shaping distortions. The model then adjusts the joint commands to compensate for the predicted errors. The entire framework has been programmed in MATLAB, and simulation results prove that the proposed framework can significantly reduce contour errors in robotic machining.