An unsupervised prediction of robotic machining error for new tasks under historical tasks knowledge distillation

Industrial robots have become an important machining equipment after machine tools in recent years, due to the advantages of, large workspace, and flexible work modes. However, the weak rigidity brought by the serial configuration leads the robot to shuffle from large errors. In addition, under the coupling of external time-varying dynamic loads and position-dependent characteristics, the robot machining error shows significant condition sensitivity. Data-driven becomes an effective mean. However, the performance of data-driven models is severely affected by the differences in working conditions，such as machining parameters, workpiece shape, etc. To this end, a new tasks unsupervised prediction method for robotic machining error prediction with historical knowledge distillation is proposed, in which the knowledge of historical tasks is distilled by a lightweight model, which achieves the transfer and reuse of domain knowledge, and improves the generalization ability of data-driven models for similar tasks. Based on eight historical tasks, robotic machining error prediction was performed for two new tasks, one with different parameters and the other with different shape. The effectiveness of the proposed method is fully verified by analyzing and comparing. Further, the scalable and maintainable digital twin system is initially constructed in order to better fulfill the needs of robotic manufacturing. The proposed method can provide a strong impetus to the development of knowledge-based intelligent manufacturing for robots.