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Digital twin paradigm: A systematic literature review
- Source :
- Computers in Industry, Computers in Industry, Elsevier, 2021, 130, pp.103469. ⟨10.1016/j.compind.2021.103469⟩
- Publication Year :
- 2021
- Publisher :
- HAL CCSD, 2021.
-
Abstract
- International audience; Manufacturing enterprises are facing the need to align themselves to the new information technologies (IT) and respond to the new challenges of variable market demand. One of the key enablers of this IT revolution toward Smart Manufacturing is the digital twin (DT). It embeds a “virtual” image of the reality constantly synchronized with the real operating scenario to provide sound information (knowledge model) to reality interpretation model to draw sound decisions. The paper aims at providing an up-to date picture of the main DT components, their features and interaction problems. The paper aims at clearly tracing the ongoing research and technical challenges in conceiving and building DTs as well, according to different application domains and related technologies. To this purpose, the main questions answered here are: ‘What is a Digital Twin?’; ‘Where is appropriate to use a Digital Twin?’; ‘When has a Digital Twin to be developed?’; ‘Why should a Digital Twin be used?’; ‘How to design and implement a Digital Twin?’; ‘What are the main challenges of implementing a Digital Twin?’. This study tries to answer to the previous questions funding on a wide systematic literature review of scientific research, tools, and technicalities in different application domains.
- Subjects :
- 0209 industrial biotechnology
Cyber-Physical Systems
General Computer Science
Industry 4.0
business.industry
Computer science
Interpretation (philosophy)
General Engineering
Cyber-physical system
Information technology
02 engineering and technology
[INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering
Data science
Supply and demand
Variable (computer science)
Digital Twin
020901 industrial engineering & automation
Systematic review
0202 electrical engineering, electronic engineering, information engineering
Key (cryptography)
Predictive manufacturing
020201 artificial intelligence & image processing
Digital twin
Cyber-physical systems
business
Subjects
Details
- Language :
- English
- ISSN :
- 01663615
- Database :
- OpenAIRE
- Journal :
- Computers in Industry, Computers in Industry, Elsevier, 2021, 130, pp.103469. ⟨10.1016/j.compind.2021.103469⟩
- Accession number :
- edsair.doi.dedup.....7bac1ae7477fbd116e51a7fc09b72540
- Full Text :
- https://doi.org/10.1016/j.compind.2021.103469⟩