SUMMARY

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 the arm configuration changes within the workspace. In this dissertation, 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 presented and validated. The data-driven modeling approach for prediction of the robot’s tool tip vibrations is applied towards toolpath planning in milling. The results show that the data-driven model can serve as a useful tool for understanding and optimizing the tool tip vibrations produced in robotic milling. In addition, the data-driven model is used to create an optimal pose-dependent feedback controller to improve the positioning accuracy of 6-dof industrial robots in the presence of tool tip vibrations during milling. Finally, a hybrid modelling methodology to improve the accuracy of the data-driven model previously calibrated from impact hammer tests using the robot’s Frequency Response Function (FRF) derived from continuously monitored force and tool tip vibrations measured during milling is shown to improve prediction accuracy with minimal training time.