An assembly process planning pipeline for industrial electronic equipment based on knowledge graph with bidirectional extracted knowledge from historical process documents

Youzi Xiao (), Shuai Zheng (), Jiewu Leng (), Ruibo Gao (), Zihao Fu () and Jun Hong ()
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Youzi Xiao: Xi’an Jiaotong University
Shuai Zheng: Xi’an Jiaotong University
Jiewu Leng: Guangdong University of Technology
Ruibo Gao: Xi’an Jiaotong University
Zihao Fu: Xi’an Jiaotong University
Jun Hong: Xi’an Jiaotong University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 5, No 36, 3647-3667

Abstract: Abstract Assembly is an essential stage in industrial electronic equipment manufacturing and needs to meet the complexity of manufacturing. Therefore, the assembly process planning for industrial electronic equipment still relies on the experiences of planners. The advent of knowledge graphs brings an opportunity to achieve automated assembly process planning. Thus, extracting process knowledge from historical assembly process documents and constructing assembly process knowledge graphs are indispensable. However, the complexity of industrial electronic equipment manufacturing leads to assembly process documents containing more complex assembly relations, longer texts, and high-density assembly entities. These characteristics pose challenges to assembly process knowledge extraction and knowledge graph modeling. The confidentiality of assembly process documents further hinders the development of this field. To address these challenges, we propose a pipeline for achieving assembly process planning from historical assembly process documents. First, we construct an assembly process dataset using historical assembly process documents from an industrial electronic equipment enterprise. Then, we propose a global relation-driven bidirectional extraction model, which automatically constructs the assembly process knowledge graph. In addition, we also propose a knowledge graph-based matching and searching method to support process planning. The proposed model is evaluated on the constructed dataset and a publicly accessible equipment fault diagnostic dataset, achieving F1-scores of 92.9% and 87.9%, respectively. Experimental results demonstrate that the proposed model achieves state-of-the-art performance on both datasets. Furthermore, we construct an assembly process knowledge graph for industrial electronic equipment and perform assembly process planning, which validates the feasibility of our pipeline.

Keywords: Assembly process knowledge graph; Process knowledge extraction; Assembly process knowledge; Assembly process planning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02423-1

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