A Knowledge Push Method of Complex Product Assembly Process Design Based on Distillation Model-Based Dynamically Enhanced Graph and Bayesian Network

Under the paradigm of Industry 5.0, intelligent manufacturing transcends mere efficiency enhancement by emphasizing human-machine collaboration, where human expertise plays a central role in assembly processes. Despite advancements in intelligent and digital technologies, assembly process design still heavily relies on manual knowledge reuse, and inefficiencies and inconsistent quality in process documentation are caused. To address the aforementioned issues, this paper proposes a knowledge push method of complex product assembly process design based on distillation model-based dynamically enhanced graph and Bayesian network. First, an initial knowledge graph is constructed using a BERT-BiLSTM-CRF model trained with integrated human expertise and a fine-tuned large language model. Then, a confidence-based dynamic weighted fusion strategy is employed to achieve dynamic incremental construction of the knowledge graph with low resource consumption. Subsequently, a Bayesian network model is constructed based on the relationships between assembly components, assembly features, and operations. Bayesian network reasoning is used to push assembly process knowledge under different design requirements. Finally, the feasibility of the Bayesian network construction method and the effectiveness of Bayesian network reasoning are verified through a specific example, significantly improving the utilization of assembly process knowledge and the efficiency of assembly process design.