Active vibration suppression in robotic milling using optimal control. (May 2020)

• Record Type: Journal Article
• Authors: Nguyen, Vinh; Johnson, Joshua; Melkote, Shreyes
• Is Part Of: International journal of machine tools & manufacture. Volume 152(2020)
• Publication Date: 2020-05
• Subjects: Robotic milling -- Gaussian process regression -- Linear quadratic regulator -- Disturbance rejection; Machine-tools -- Periodicals; Manufacturing processes -- Periodicals; Machines-outils -- Périodiques; Fabrication -- Périodiques; Electronic journals; 621.902
• DOI: 10.1016/j.ijmachtools.2020.103541
• Languages: English
• ISSNs: 0890-6955

Abstract

Abstract: Six degree-of-freedom (6-dof) industrial robots are attractive alternatives to Computer Numerical Control (CNC) machine tools for milling of large parts because of their low-cost, greater versatility, and larger work volume. However, 6-dof industrial robots are significantly more compliant than CNC machine tools, which makes them prone to vibrations during milling. An additional complexity of industrial robots is their pose-dependent vibration characteristics. This paper presents a pose-dependent optimal control methodology to actively suppress tool tip vibrations generated by the periodic milling forces in robotic milling. Discretely sampled robot structural modal parameters as a function of robot configuration (pose) are used to develop a data-driven Gaussian Process Regression (GPR) model. The model is then utilized to solve the Linear Quadratic Regulator (LQR) optimal control problem to obtain pose-dependent controller gains necessary for vibration suppression. The pose-dependent controller is implemented on a 6-dof industrial robot and its performance evaluated through process-independent offset mass experiments and through milling experiments. The methodology is shown to be effective in decreasing the tool tip vibrations and improving the machining accuracy in robotic milling. Graphical abstract: Image 1 Highlights: A pose-dependent optimal controller was implemented using a data-driven model. The optimal control gains were shown to change with robot arm configuration. The control methodology was shown to suppress vibrations in robotic milling.