中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
  5. 英文
Knowledge-based digital twin system: Using a knowlege-driven approach for manufacturing process modeling

文献类型：期刊论文

作者Su, Chang1; Han, Yong1; Tang, Xin2,3; Jiang, Qi1; Wang, Tao1; He, Qingchen1
刊名COMPUTERS IN INDUSTRY
出版日期2024-08-01
卷号159页码:21
关键词Manufacturing process Knowledge-based digital twin system Knowledge graph Knowledge-driven modeling approach Knowledge inference
ISSN号0166-3615
DOI10.1016/j.compind.2024.104101
英文摘要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.
资助项目National Key R & D Program of China[2020YFB1710400]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001236799800001
出版者ELSEVIER
源URL[http://119.78.100.204/handle/2XEOYT63/40056]  
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.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。