Key Takeaways & Executive Findings
- •• Proposes a novel knowledge push method integrating distillation model-based dynamic graph enhancement and Bayesian network reasoning for complex product assembly process design. • Achieves dynamic incremental construction of knowledge graphs with low resource consumption using a confidence-based weighted fusion strategy. • Demonstrates significant improvement in assembly process knowledge utilization and design efficiency through a specific example. • Aligns with Industry 5.0 human-centric intelligent manufacturing paradigm, enhancing human-machine collaboration in assembly.
Abstract
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.
1. Introduction
With the rapid development of generative artificial intelligence (AI), the deep integration of general artificial intelligence with automation and information technology is profoundly changing the landscape of intelligent manufacturing. The human-centric intelligent manufacturing is one of the main potential emerging paradigms. The core of human-centric should be that AI will deeply assist and provide recommendations at the stage of perception, analysis, decision-making, and execution. While participating, humans will play a decisive role and avert potential issues that may arise (Figure 1).
Complex products are characterized by structurally complex designs, technologically advanced manufacturing, and intricate development processes [1], such as satellites and aircraft. In the development of complex products, assembly operations account for 20% to 70% of total workload, averaging 45% [2]. This significant proportion necessitates a balance between automation efficiency and human ergonomic requirements in process design systems [3]. Assembly process design constitutes a critical component of complex product manufacturing, aiming to generate high-quality assembly process information [4]. Essentially, the creation or planning of assembly processes represents a cognitive operation that relies heavily on expert knowledge and experience. During complex product assembly process design, substantial amounts of assembly process knowledge with intricate semantic relationships are utilized [5]. However, current knowledge reuse in assembly process design remains overly dependent on manual experience, resulting in inefficient documentation processes and inconsistent quality standards.
With the advent of Industry 5.0, the objective of intelligent manufacturing has shifted from technology-driven production to knowledge-driven efficiency enhancement through human-machine collaboration [6]. Under this paradigm, assembly, as the final stage in the development of complex products, not only focuses on technical implementation but also emphasizes the deep integration of expert knowledge, human experience, and artificial intelligence to enhance operational efficiency [7]. The key enabling tech
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Fengque Pei, Yaojie Lin, Jianhua Liu, Cunbo Zhuang, Sikuan Zhai (2025). A Knowledge Push Method of Complex Product Assembly Process Design Based on Distillation Model-Based Dynamically Enhanced Graph and Bayesian Network. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01275-x
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a knowledge push method for complex product assembly process design that integrates a distillation model-based dynamically enhanced graph and Bayesian network, enabling dynamic incremental knowledge graph construction and efficient knowledge push under different design requirements.
How does the proposed method improve assembly process design?
The method significantly improves the utilization of assembly process knowledge and the efficiency of assembly process design by automating knowledge graph construction and using Bayesian network reasoning to push relevant knowledge, reducing manual effort and inconsistency.
What role does the large language model play in this research?
A fine-tuned large language model is used in conjunction with a BERT-BiLSTM-CRF model to construct the initial knowledge graph, integrating human expertise and enhancing the extraction of assembly process knowledge.
How is the knowledge graph dynamically updated?
The knowledge graph is dynamically updated using a confidence-based dynamic weighted fusion strategy, which allows incremental construction with low resource consumption, ensuring the graph remains current and accurate.
What is the significance of this work in the context of Industry 5.0?
This work aligns with Industry 5.0's human-centric intelligent manufacturing paradigm by enhancing human-machine collaboration, where AI assists in assembly process design while humans retain decisive roles, improving overall operational efficiency.
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