Multi-process digital twin closed-loop machining through shape-feature state update and error propagation knowledge graph

Machining digital twin systems represent a novel platform for achieving full autonomy in machine tools. However, in multi-process machining, error propagation between different processes presents significant challenges to current digital twin systems: real-time model updates, error evolution analysis and optimization. To address these issues, this study proposes a method for rapid state updates of shape features and the construction of an error propagation knowledge graph, enabling dynamic control in a multi-process machining digital twin system. First, a state update method based on multi-level voxel computation is introduced, utilizing the transformation relationship between cutter-workpiece engagement (CWE) and voxels to ensure rapid updates of multi-process digital twin models. Second, digital threads for multi-process machining are developed to identify machining states and track errors. To analyze error evolution and implement effective control measures, an error propagation knowledge graph based on Taylor expansion model is constructed, enabling closed-loop control. Finally, the proposed system was validated for its advancement in error control and closed-loop optimization through the machining of typical multi-process components in the transportation field. The results of comparative experiments and ablation experiments indicate that the error updating efficiency based on multi-level voxel computation threads improved by an average of 59.26% compared to other benchmarks. The error propagation knowledge graph based on the Taylor model achieved a 42.75% improvement in reasoning accuracy compared to the ablated Taylor model. The developed digital twin closed-loop optimization system successfully ensured that the errors in 1280 processes of the case study remained within the tolerance range.