Key Takeaways & Executive Findings
- •• Introduces a novel method (APKG-CP) for automatically constructing assembly process knowledge graphs from complex product documents using text mining, addressing high costs and low efficiency. • Employs a Bert-BiLSTM-CRF model to extract entities and relationships from unstructured assembly process text, enhancing semantic representation. • Validates the approach with an aerospace enterprise case, demonstrating successful knowledge graph construction and visualization. • Shows practical effectiveness by integrating the knowledge graph with an assembly process preparation system, improving process design.
Abstract
Efficient preparation and assembly guidance for complex products relies heavily on semantic information in assembly process documents. This information encompasses various levels of elements and complex semantic relationships. However, there is currently a scarcity of effective modeling techniques to express these documents’ inherent assembly process knowledge. This study introduces a method for constructing an Assembly Process Knowledge Graph of Complex Products (APKG-CP) utilizing text mining techniques to tackle the challenges of high costs, low efficiency, and difficulty reusing process knowledge. Developing the assembly process knowledge graph involves categorizing entity and relationship classes from multiple levels. The Bert-BiLSTM-CRF model integrates BERT (bidirectional encoder representations from transformers), BiLSTM (bidirectional long short-term memory), and CRF (conditional random field) to extract knowledge entities and relationships in assembly process documents automatically. Furthermore, the knowledge fusion method automatically instantiates the assembly process knowledge graph. The proposed construction method is validated by constructing and visualizing an assembly process knowledge graph using data from an aerospace enterprise as an example. Integrating the knowledge graph with the assembly process preparation system demonstrates its effectiveness for process design.
1. Introduction
Assembly time constitutes a significant portion of the production process, accounting for between 20% and 70%. This aspect directly influences the final product’s performance [1, 2]. In sectors such as aerospace engineering, the assembly of complex products typically follows a discrete workflow that involves small production lots, long assembly cycles, and complex information flow [3]. Assembly process documentation is usually provided in electronic formats and textual materials, which assist assemblers in completing operational tasks. Such documentation offers vital insights into process execution challenges, assembly rules, and implementation methods [4].
Many manufacturing companies have recently embraced digital and intelligent transformation, creating numerous assembly process documents managed through product data management systems [5]. These documents contain a wealth of assembly information, including practical experience and expert knowledge. The documents detailing the assembly process for complex products possess three main characteristics: they are predominantly textual, comprehensive, and complex; assembly process information for specific products is highly similar, and their data structure varies, with the core content comprising unstructured natural text.
The planning and creation of an assembly process operation heavily relies on cognitive operations rooted in expert knowledge and experience. Process designers often utilize existing assembly process documentation to develop new documents accurately and efficiently. Standardizing their representation is essential to maximize the utility of expertise and knowledge embedded in these assembly documents. Scholars have turned to ontology modeling techniques and knowledge graph techniques to standardize the representation of various information within the product development process [6, 7]. However, assembly process documents are semi-structured, presenting structured and unstructured data. Nonetheless, extracting domain expertise from the vast amount of unstructured data in process documents can be challenging when dealing with complex and variable products. Traditional knowledge representation methods typically neglect unstructured data within assembly processes, concentrating primarily on sequential relationships between assembly processes and steps. This limitation inhibits the expression of sequential and semantic relationships in assembly process documents, resulting in the loss of valuable semantic information about specific assembly task instructions. Thus, new knowledge representation methods are needed.
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Kunping Li, Jianhua Liu, Sikuan Zhai, Cunbo Zhuang, Fengque Pei (2025). Automatic Generation Method of Knowledge Graph for Complex Product Assembly Processes Based on Text Mining. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01284-w
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a method for automatically constructing an Assembly Process Knowledge Graph of Complex Products (APKG-CP) using text mining, which addresses the challenges of high costs, low efficiency, and difficulty in reusing process knowledge.
How does the proposed method extract knowledge from assembly process documents?
The method uses a Bert-BiLSTM-CRF model, which integrates BERT, BiLSTM, and CRF, to automatically extract knowledge entities and relationships from assembly process documents.
What is the significance of the knowledge graph in assembly process design?
The knowledge graph enhances semantic representation of assembly process information, enabling more efficient and accurate process design by integrating with assembly process preparation systems.
How was the proposed method validated?
The method was validated by constructing and visualizing an assembly process knowledge graph using data from an aerospace enterprise, demonstrating its effectiveness.
What are the key technologies used in this study?
Key technologies include text mining, BERT, BiLSTM, CRF, ontology modeling, and knowledge graph construction.
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