Knowledge-graph-based multi-domain model integration method for digital-twin workshops

被引:16
作者
Wang, Xiangdong [1 ]
Hu, Xiaofeng [2 ]
Ren, Zijie [3 ]
Tian, Tianci [3 ]
Wan, Jiafu [3 ]
机构
[1] South China Univ Technol, Shien Ming Wu Sch Intelligent Engn, Guangzhou, Peoples R China
[2] Shanghai Jiao Tong Univ, Sch Mech Engn, Shanghai, Peoples R China
[3] South China Univ Technol, Sch Mech & Automot Engn, Guangdong Prov Key Lab Tech & Equipment Macromol A, Guangzhou, Peoples R China
关键词
Knowledge graph; Digital twin workshop; Model integration; Knowledge reasoning; Shipbuilding industry;
D O I
10.1007/s00170-023-11874-4
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The digital twin workshop is a new workshop operation paradigm that enables precise decision-making by fusing virtual and physical space. As a complex manufacturing system, the digital twin model of the workshop must integrate models from different domains in order to provide personalized services. The interoperability of multi-domain models and the dynamic update of parameters become obstacles. In this paper, a knowledge graph (KG)-based multi-domain model integration method for digital twin workshops is proposed. The multi-domain model integration architecture based on KG is consisted of model element, model ontology, model data, semantic integration, and network connection. Then, the KG of multi-domain model for design, manufacturing, and simulation is constructed through ontology modeling and knowledge extraction. On this basis, multi-domain model integration is realized through semantic inference and knowledge query. The model parameters are updated through file exchange during the dynamic simulation. Finally, multiple scenarios in the subassembly workshop for hull construction are used to verify the efficacy of the proposed method. During the assembly and welding of hull parts, the integration of the product model, equipment model, and simulation model is realized, which assists in meeting the service requirements of multiple business scenarios.
引用
收藏
页码:405 / 421
页数:17
相关论文
共 37 条
[1]  
Al Faruque MA, 2021, PROCEEDINGS OF THE 2021 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2021), P440, DOI 10.23919/DATE51398.2021.9474166
[2]   The modelling and operations for the digital twin in the context of manufacturing [J].
Bao, Jinsong ;
Guo, Dongsheng ;
Li, Jie ;
Zhang, Jie .
ENTERPRISE INFORMATION SYSTEMS, 2019, 13 (04) :534-556
[3]   OntoSTEP: Enriching product model data using ontologies [J].
Barbau, Raphael ;
Krima, Sylvere ;
Rachuri, Sudarsan ;
Narayanan, Anantha ;
Fiorentini, Xenia ;
Foufou, Sebti ;
Sriram, Ram D. .
COMPUTER-AIDED DESIGN, 2012, 44 (06) :575-590
[4]   Improving Cognitive Ability of Edge Intelligent IIoT through Machine Learning [J].
Chen, Baotong ;
Wan, Jiafu ;
Lan, Yanting ;
Imran, Muhammad ;
Li, Di ;
Guizani, Nadra .
IEEE NETWORK, 2019, 33 (05) :61-67
[5]   Physics-based simulation ontology: an ontology to support modelling and reuse of data for physics-based simulation [J].
Cheong, Hyunmin ;
Butscher, Adrian .
JOURNAL OF ENGINEERING DESIGN, 2019, 30 (10-12) :655-687
[6]  
Grangel-González I, 2020, IEEE INT C EMERG, P93, DOI [10.1109/etfa46521.2020.9212156, 10.1109/ETFA46521.2020.9212156]
[7]   A digital twin-based layout optimization method for discrete manufacturing workshop [J].
Guo, Hongfei ;
Zhu, Yingxin ;
Zhang, Yu ;
Ren, Yaping ;
Chen, Minshi ;
Zhang, Rui .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2021, 112 (5-6) :1307-1318
[8]  
Hitzler P., 2009, Foundations of Semantic Web Technologies, DOI DOI 10.1201/9781420090512
[9]   An approach to capturing and reusing tacit design knowledge using relational learning for knowledge graphs [J].
Jia, Jia ;
Zhang, Yingzhong ;
Saad, Mohamed .
ADVANCED ENGINEERING INFORMATICS, 2022, 51
[10]  
Kharlamov E, 2018, IEEE INT CONF BIG DA, P4189, DOI 10.1109/BigData.2018.8622503