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

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.

Optimization of redundant degree of freedom in robot milling considering chatter stability

“…In recent years, two methods have been used to predict the frequency response function of the tool tip of the robot: (1)Data modeling based on experiment [22][23][24]; (2)Theoretical modeling based on finite element method [17].…”

Improved subspace modal identification of industrial robots

“…The wide application of industrial robots reduces production costs while greatly improving production efficiency (K. Wang et al, 2014). However, due to low stiffness and high compliance problem, industrial robots will be inevitably easy to be vibrated by self‐excitation or periodic force dependent on workspace configuration (Nguyen et al, 2019; Nguyen & Melkote, 2020). To improve the operation accuracy and reliability of industrial robots, the knowledge of the robot's modal properties should be accurately required to design the appropriate working path and optimize the control parameters.…”

A two-step machining and active learning approach for right-first-time robotic countersinking through in-process error compensation and prediction of depth of cuts

“…Machine Learning (ML)-based error compensation approaches [23][24][25] have also been proposed. Zhu et al [23] applied artificial neural networks for mapping and compensation of robot positioning errors in robotic drilling of aircraft panels.…”

“…Wang et al [24] proposed a statistical learning control method based on Gaussian Process Regression (GPR) [26] for trajectory tracking in laser cutting applications, where GPR models were used for both inverse robot dynamics and kinematics to compensate the torque and motor reference, respectively. The GPR framework has also been explored by Nguyen et al [25] to model the dynamic properties of a typical six Degrees-of-Freedom (DoF) industrial robot used for milling operations. Other recent works that have reported the use of data-driven approaches for error prediction and compensation, focus on thermally-induced errors of conventional machining processes [27,28].…”