Pose-dependent Cutting Force Identification for Robotic Milling

Abstract

Cutting force identification is critical to improving industrial robot performance and reducing machining vibration. However, most indirect identification methods of cutting force are not applicable since the dynamic characteristics of the robotic milling system vary with the robot pose. In this paper, a novel pose-dependent method is proposed to identify the cutting force using the acceleration signal generated by robotic milling. Firstly, the modal parameters of the robot at different machining points are used as a training dataset to develop the Gaussian Process Regression (GPR) model. Next, the modal parameters predicted by the GPR model are used to optimize the cutting force estimation based on the minimum variance unbiased estimate method. Then, the Kalman filter method is used to update the covariance matrix of the cutting force identification error and the state estimation error. Lastly, the proposed method is verified with the experiment, and the results show that the identification error and time are acceptable under the condition of variable robot pose.