Digital twin-driven staged error prediction and compensation framework for the whole process of robotic machining

Robotic machining has become another important machining paradigm after CNC machine tools. However, robot error has always been an important constraint in its progress towards high quality demand scenarios due to characteristics such as weak rigidity and pose dependence. Numerous scholars have carried out rich work around errors in robotic machining systems, and these studies have achieved excellent results in robot localization, trajectory continuous motion, and machining operations. However, due to the complexity of the robot machining system, the robot error has differentiated performance at different stages, and it is difficult to guarantee the global accuracy of the robot by focusing on and controlling a certain kind of error in a discrete manner. For this reason, a digital twin-driven staged error prediction and compensation framework for the whole robot machining process is constructed. In this framework, the whole process of robot machining is divided into three stages with significant differences: point planning, trajectory planning and material removal. And the error prediction function block in each stage is constructed for the error characteristics (distribution skew, error step, spatial-temporal coupling). For error compensation, a staged error compensation strategy is constructed from three aspects: offline point position, robot body and external three-axis platform, respectively. The constructed system was case-validated in the robotic machining of curved parts. All stages of the error prediction models show high prediction accuracy, and the excellent performance of the staged prediction models is verified by comparing with the classical prediction models. For the error compensation, the designed system is utilized to ensure that the robotic machining system provides a double guarantee on the robot end and the machining quality, the point position absolute error is controlled at 0.109 mm, the orientation error is controlled at 0.028°, the trajectory position error is controlled at 0.067 mm, the orientation error is controlled at 0.031°, and the final part machining error is controlled at 0.036 mm, which is almost approximates the repeatable positioning accuracy of the robot. The proposed framework realizes the system-level sensing and control of the robot machining system error, and provides a unified system framework for the subsequent research of related unit methods, which is conducive to promoting the development of robot machining to high-quality requirement scenarios.