Accuracy Optimization of Robotic Machining Using Grey-Box Modeling and Simulation Planning Assistance

The aim of this paper is to develop an approach to increase the accuracy of industrial robots for machining processes. During machining tasks, process forces displace the end effector of the robot. A simulation of the various process influences is therefore necessary to ensure stable machining during production planning in optimizing the process parameters. Realistic simulations require precise dynamics and stiffness models of the robot. Regarding the dynamics, the frictional component is highly complex and difficult to model. Therefore, this paper follows a grey-box approach to combine the advantages of the state-of-the-art Lund–Grenoble model (white-box) with those of a data-driven one (black-box) in the first part. The resulting grey-box LuGre model proves to be superior to the white- and black-box models. In the second part, a model-based simulation planning assistance tool is developed, which makes use of the grey-box LuGre model. The simulation assistance provides the manufacturing planner with process knowledge using the identified robot and cutting force models. Furthermore, it provides optimization methods such as a switching point analysis. Finally, the assistance tool gives predictions about the machining result and a process evaluation. The third part of the paper shows the evaluation of the simulation assistance on a real machining process and workpiece, showing an increase in accuracy using the tool.