Increasing Time-Efficiency and Accuracy of Robotic Machining Processes Using Model-Based Adaptive Force Control

Machining processes in the industry of today are rarely performed using industrial robots. In the cases where robots are used, machining is often performed using position control with a conservative feed-rate, to avoid excessive process forces. There is a great benefit in controlling the process forces instead, so as to improve the time-efficiency by applying the maximum allowed force, and thus removing the maximum amount of material per time unit. This paper presents a novel adaptive force controller, based on a derived model of the machining process and an identified model of the robot dynamics. The controller is evaluated in both simulation and an experimental setup. Further, industrial robots generally suffer from low stiffness, which can cause the robot to deviate from the desired path because of strong process forces. The present paper solves this by employing a stiffness model to continuously modify the robot trajectory to compensate for the deviations. The adaptive force controller in combination with the stiffness compensation is evaluated in experiments, with satisfying results.