An assembly process planning pipeline for industrial electronic equipment based on knowledge graph with bidirectional extracted knowledge from historical process documents
IF 5.9 2区 工程技术Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Youzi Xiao, Shuai Zheng, Jiewu Leng, Ruibo Gao, Zihao Fu, Jun Hong
{"title":"An assembly process planning pipeline for industrial electronic equipment based on knowledge graph with bidirectional extracted knowledge from historical process documents","authors":"Youzi Xiao, Shuai Zheng, Jiewu Leng, Ruibo Gao, Zihao Fu, Jun Hong","doi":"10.1007/s10845-024-02423-1","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"25 1","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s10845-024-02423-1","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.