关键词: 参数推荐, 知识图谱, 钻铆工艺, 知识推理, 工艺知识建模

Abstract: To address the challenge of intelligent decision-making in dual-robot drilling and riveting, which is often hin-dered by reliance on manual expertise and fragmented process knowledge, this study proposes an intelligent parameter recommendation method that integrates knowledge graphs with rule reasoning. A multi-layer knowledge graph for dual-robot drilling and riveting is first constructed, comprising 292 entities and 711 rela-tionships across five layers—tool, workpiece, process, feature, and instance—to enable multimodal semantic associations among material properties, process parameters, and quality indicators. Building on this, a two-stage reasoning framework is introduced: similar cases are retrieved through a weighted cosine similarity algo-rithm, where the weights are determined using the Analytic Hierarchy Process, and a priority-based rule engine is applied for parameter constraints and conflict resolution, thereby improving the accuracy and consistency of parameter recommendations. Furthermore, a dynamic confidence evaluation model is developed by incorporat-ing data- and rule-based constraints, establishing a four-dimensional assessment along the axes of data scale, data quality, data distribution, and rule conflicts. This dynamic weighting mechanism enhances the interpreta-bility of the recommendation results. Experimental validation on a dual-robot drilling and riveting work-station at COMAC Shanghai Aircraft Manufacturing Co., Ltd. demonstrates that the proposed method achieves a comprehensive confidence level of 0.93, with the recommended parameters satisfying physical process con-straints. Overall, this method provides an effective approach for capturing and reusing process knowledge while supporting intelligent decision-making in complex manufacturing scenarios.

Key words: Parameter Recommendation, Knowledge graph, Drilling and Riveting Process, Knowledge Reasoning, Knowledge Modeling