SUMMARY

The use of industrial robots in machining operations, such as milling, is an area of growing interest due to potential workflow and efficiency benefits. However, the inherent mechanical design of robot manipulators results in low stiffness and easy-to-excite dynamics when compared to the traditionally used CNC machines. While research exists to compensate for deficiencies in robot manipulators, such as trajectory planning, online and offline error compensation, no integrated solution combining process-force compensation, robotic trajectory planning, and online error compensation exists, as would be required for industrial settings. This thesis introduces a deflection-limited trajectory planning algorithm for curvilinear slotting and linear peripheral milling cuts. The research purpose is to develop a solution in which feed-rate optimized trajectory minimizes part errors when milling with an industrial robot. Thus, given a set of points to be approximated into a path, the methodology in this thesis generates a process-aware trajectory in which feed-rate has been optimized to meet a user-specified deflection limit. The trajectory is formatted to be compatible with a closed-loop feedback and communication system with an industrial robot. Experiments are conducted using a large (range of 2855 mm), industrial robot milling system controlled by a closed-loop, laser tracker feedback system. Experimental data supports that the optimized trajectory provides better part accuracy and surface roughness than that of the non-optimized case. Furthermore, the optimized trajectory executed by the closed-loop system maintains better positional accuracy than the open-loop, native robot controller using native motion types. Thus, the merit of a process dependent trajectory planner is argued, and future work for improvements and use-case generalization is suggested.