Robotic machining quality enhancement via physics-informed error prediction and collaborative compensation

Industrial robots have become indispensable machining equipment alongside machine tools due to their large workspace and high flexibility. However, their inherent structural compliance and geometric imperfections introduce spatially distributed pose errors, particularly in high-precision applications. Current robot error compensation is mostly based on single-source adjustment of the body, which is affected by the spatial sensitivity relationship between joint space and pose errors. For this reason, a hybrid manufacturing system integrating a robot and an external linear cell was constructed. Based on this system, a physical-informed approach for distributed prediction and collaborative compensation of robot machining quality is proposed. Firstly, a novel spatial–temporal attention-based sensing model was built to predict the robot pose errors. Secondly, an unsupervised distributed prediction module with physics informatization is constructed based on the sensitivity analysis of joint pose errors. Finally, the collaborative compensation is realized by a hybrid manufacturing system containing the robot itself and the external linear axes. Experimental validation on large-curvature surface machining demonstrates the system’s ability to simultaneously predict and compensate machining quality deviations, achieving positioning accuracy with maximum/average errors of 0.14 mm/0.03 mm respectively. The physics-based approach significantly outperforms conventional methods by coordinating distributed prediction with collaborative compensation, reducing sensitive joint adjustments while suppressing regenerative errors. These advancements establish a new paradigm for precision robotic machining in industrial applications.