Data-Driven Modeling of the Modal Properties of a Six-Degrees-of-Freedom Industrial Robot and Its Application to Robotic Milling

Abstract

Abstract This paper presents a Gaussian process regression (GPR)-based approach to model the dynamic properties of a six-degree-of-freedom (6-DOF) industrial robot within its workspace. Discretely sampled modal parameters (modal frequency, modal stiffness, modal damping coefficient) of the robot structure determined through experimental modal analysis are used to develop the GPR model, which is then evaluated for its ability to accurately predict the modal parameters at different points in the workspace. The validation results show that the model captures the significant trends in the modal parameters within the sampling space but exhibits greater errors in regions further from the robot base. The results of the GPR model are also compared with those derived from an analytical model of the robot tool tip dynamics. The analytical model is found to overestimate the robot’s stiffness, especially in extended arm configurations, and to underestimate the natural frequency. The average peak-to-valley vibrations predicted by the GPR model during robotic end milling are compared with experimental results. The model-predicted peak-to-valley vibrations follow the measured values with a maximum error of 0.028 mm in the wall and floor surface directions. The results show that the GPR model presented in this paper can serve as a useful tool for understanding and optimizing the tool tip vibrations produced in robotic milling.