Construction method and application of knowledge graph for process defect of injection molding products

被引：0
|
作者：
Ge R.-F. [1 ]
Ren Z.-G. [1 ]
Lin J.-H. [1 ]
Lin Y. [1 ]
Gao Z.-B. [1 ]
机构：
[1] Guangdong-Hong Kong-Macao Intelligent Joint Laboratory for Discrete Manufacturing, School of Automation, Guangdong University of Technology, Guangdong, Guangzhou
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2024年 / 41卷 / 03期
基金：
中国国家自然科学基金;
关键词：
expert knowledge; fault diagnosis; knowledge extraction; knowledge graph; ontology model;
D O I：
10.7641/CTA.2023.20959
中图分类号：
学科分类号：
摘要：
In response to the deficiencies of low efficiency and high costs associated with the existing manual diagnosis of injection molding product defects, this paper proposes a method for constructing a knowledge graph for injection molding product defects and its application. The objective is to represent expert knowledge by using a knowledge graph and utilize knowledge graph-based vertical retrieval techniques to address the difficulties in fault troubleshooting and localization. Firstly, a corpus of fault resolution solution texts is built based on the multiple heterogeneous sources, and a knowledge ontology model is constructed. Secondly, a knowledge extraction model for unstructured texts is employed to automatically extract relevant expert knowledge regarding product defects from the original corpus. Finally, the Neo4j graph database is used to implement knowledge storage and the construction of a visualized knowledge graph. In the constructed knowledge graph, applications such as intelligent knowledge search, fault diagnosis, and process card analysis are explored and implemented, demonstrating the promising application prospects of knowledge graph technology in the field of injection molding. © 2024 South China University of Technology. All rights reserved.
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页码：577 / 585
页数：8
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