SUBJECT: Ph.D. Proposal Presentation
BY: Vinh Nguyen
TIME: Tuesday, September 24, 2019, 10:00 a.m.
PLACE: MARC Building, 201
TITLE: Accuracy Improvement in Robotic Milling through Data-Driven Modelling and Control
COMMITTEE: Dr. Shreyes N. Melkote, Chair (ME)
Dr. Steven Liang (ME)
Dr. Thomas Kurfess (ME)
Dr. Roshan Joseph (ISyE)
Dr. Stephen Balakirsky (GTRI)


Six degree of freedom (6-dof) articulated arm industrial robots are promising candidates for milling operations due to their low-cost and large workspace compared to Computer Numerical Control (CNC) machine tools. However, the position accuracy of industrial robots during milling is dependent on the vibratory behavior of the tool tip mounted at the robot end effector. Consequently, it is necessary to predict the robot’s tool tip vibration as it changes within the workspace.

In this thesis, a data-driven approach to model and predict the structural vibration (modal) parameters of a 6-dof industrial robot over its workspace using data from modal impact hammer tests as well as from actual operational (e.g. milling) data is proposed. We wish to apply the data-driven modeling approach for prediction of the robot’s tool tip vibrations during milling and for toolpath planning purposes. To improve the positioning accuracy of 6-dof industrial robots in the presence of tool tip vibrations, we also propose to utilize the data-driven model for on-line vibration suppression during milling. In addition, we wish to improve the accuracy of the data-driven model using the robot’s Frequency Response Function (FRF) derived from continuously monitored force and tool tip vibrations measured during the milling operation as opposed to impact hammer tests carried out at discrete points in the robot’s workspace.