Knowledge-based digital twin system: Using a knowlege-driven approach for manufacturing process modeling

doi:10.1016/j.compind.2024.104101

	Knowledge-based digital twin system: Using a knowlege-driven approach for manufacturing process modeling
	Su, Chang 1; Han, Yong 1; Tang, Xin 2,3; Jiang, Qi 1; Wang, Tao 1; He, Qingchen 1
	2024-08-01
发表期刊	COMPUTERS IN INDUSTRY
ISSN	0166-3615
卷号	159 页码:21
摘要	The Knowledge-Based Digital Twin System is a digital twin system developed on the foundation of a knowledge graph, aimed at serving the complex manufacturing process. This system embraces a knowledge-driven modeling approach, aspiring to construct a digital twin model for the manufacturing process, thereby enabling precise description, management, prediction, and optimization of the process. The core of this system lies in the comprehensive knowledge graph that encapsulates all pertinent information about the manufacturing process, facilitating dynamic modeling and iteration through knowledge matching and inference within the knowledge, geometry, and decision model. This approach not only ensures consistency across models but also addresses the challenge of coupling multi-source heterogeneous information, creating a holistic and precise information model. As the manufacturing process deepens and knowledge accumulates, the model 's understanding of the process progressively enhances, promoting self-evolution and continuous optimization. The developed knowledgedecision-geometry model acts as the ontological layer within the digital twin framework, laying a foundational conceptual framework for the digital twin of the manufacturing process. Validated on an aero-engine blade production line in a factory, the results demonstrate that the knowledge model, as the core driver, enables continuous self-updating of the geometric model for an accurate depiction of the entire manufacturing process, while the decision model provides deep insights for decision-makers based on knowledge. The system not only effectively controls, predicts, and optimizes the manufacturing process but also continually evolves as the process advances. This research offers a new perspective on the realization of the digital twin for the manufacturing process, providing solid theoretical support with a knowledge-driven approach.
关键词	Manufacturing process Knowledge-based digital twin system Knowledge graph Knowledge-driven modeling approach Knowledge inference
DOI	10.1016/j.compind.2024.104101
收录类别	SCI
语种	英语
资助项目	National Key R & D Program of China[2020YFB1710400]
WOS研究方向	Computer Science
WOS类目	Computer Science, Interdisciplinary Applications
WOS记录号	WOS:001236799800001
出版者	ELSEVIER
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40057
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Su, Chang
作者单位	1.Ocean Univ China, Dept Informat Sci & Engn, Qingdao 266100, Peoples R China 2.North China Elect Power Univ, Control & Comp Engn, Beijing 102206, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Su, Chang,Han, Yong,Tang, Xin,et al. Knowledge-based digital twin system: Using a knowlege-driven approach for manufacturing process modeling[J]. COMPUTERS IN INDUSTRY,2024,159:21.
APA	Su, Chang,Han, Yong,Tang, Xin,Jiang, Qi,Wang, Tao,&He, Qingchen.(2024).Knowledge-based digital twin system: Using a knowlege-driven approach for manufacturing process modeling.COMPUTERS IN INDUSTRY,159,21.
MLA	Su, Chang,et al."Knowledge-based digital twin system: Using a knowlege-driven approach for manufacturing process modeling".COMPUTERS IN INDUSTRY 159(2024):21.