Knowledge Graph-Based Assembly Resource Knowledge Reuse towards Complex Product Assembly Process

Xiaolin Shi (), Xitian Tian, Jianguo Gu, Fan Yang, Liping Ma, Yun Chen and Tianyi Su
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Xiaolin Shi: College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou 121001, China
Xitian Tian: Institute of Intelligent Manufacturing, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Jianguo Gu: College of Mechanical and Electrical Engineering, Zaozhuang University, Zaozhuang 277160, China
Fan Yang: Institute of Intelligent Manufacturing, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Liping Ma: Institute of Intelligent Manufacturing, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an 710072, China
Yun Chen: College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou 121001, China
Tianyi Su: College of Mechanical Engineering and Automation, Liaoning University of Technology, Jinzhou 121001, China

Sustainability, 2022, vol. 14, issue 23, 1-16

Abstract: Assembly process designers typically confront the challenge of seeking information out of large volumes of non-structured files with a view to supporting the decision-making to be made. It is a leading concern that embedding data in text documents can hardly be retrieved semantically in order to facilitate decision-making with timely support. For tackling this gap, we propose in this paper a knowledge graph-based approach used to merge and retrieve information decided to be relevant within an engineering context. The proposed approach is to establish a multidimensional integrated assembly resource knowledge graph (ARKG) based on the structure of function-structure-assembly procedure-assembly resource, and this multidimensional integrated structure can well accomplish the retrieval of related knowledge. The upper semantic framework of ARKG is established by the assembly resource ontology model, which is a semantic-type framework involving multiple domains of knowledge to create instantiated data reflecting the full profile of the assembly resource for obtaining structured data of ARKG while avoiding the data redundancy problem. The ARKG method is validated through assembly scenario of the aircraft, and the results show the effectiveness and accuracy of the ARKG used by the assembly process designer in the assembly process design phase for retrieving the target knowledge of the assembly resources.

Keywords: knowledge graph; assembly resource; ontology; knowledge services (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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