Intelligent parameter recommendation method for dual-robot drilling and riveting with knowledge-rule fusion and dynamic confidence quantification

To address the challenge of intelligent decision-making in dual-robot drilling and riveting, which is often hindered by reliance on manual expertise and fragmented process knowledge, this study proposes an intelligent parameter recommendation method that integrates knowledge graphs with rule reasoning. Initially, a multi-layer knowledge graph for dual-robot drilling and riveting is constructed. This graph comprises 292 entities and 711 relationships, organized across five layers: tool, workpiece, process, feature, and instance. It enables multimodal semantic associations among material properties, process parameters, and quality indicators. Based on this knowledge graph, a two-stage reasoning framework is introduced. In the first stage, similar cases are retrieved using a weighted cosine similarity algorithm, where the weights are determined via the analytic hierarchy process. In the second stage, a priority-based rule engine enforces parameter constraints and resolves conflicts. This framework enhances both the accuracy and consistency of parameter recommendations. Furthermore, a dynamic confidence evaluation model is developed by incorporating 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 interpretability of the recommendation results. Experimental validation on a dual-robot drilling and riveting workstation 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 constraints. Overall, this method provides an effective approach for capturing and reusing process knowledge while supporting intelligent decision-making in complex manufacturing scenarios.