Knowledge graph and function block based Digital Twin modeling for robotic machining of large-scale components

Robotic machining is a potential method for machining large-scale components (LSCs) due to its low cost and high flexibility. However, the low stiffness of robots and complex machining process of LSCs result in a lack of alignment between the physical process and digital models, making it difficult to realize the robotic machining of LSCs. The recent Digital Twin (DT) concept shows potential in terms of representing and modeling physical processes. Therefore, this study proposes a robotic machining DT for LSCs. However, the current DT is not capable of knowledge representation, multi-source data integration, optimization algorithm implementation, and real-time control. To address these issues, Knowledge Graph (KG) and Function Block (FB) are employed in the proposed robotic machining DT. Here, robotic machining related information, such as the machining parameters and errors, is represented in the virtual space by building the KG, whereas the FBs are responsible for integrating and applying the algorithms for process execution and optimization based on real-world events. Moreover, a novel adaptive process adjustment strategy is proposed to improve the efficiency of the process execution. Finally, a prototype system of the robotic machining DT is developed and validated by an experiment on robotic milling of the assembly interface for an LSC. The results demonstrate that the robotic machining is successfully optimized and improved by the proposed method.