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Digital Twin applications toward Industry 4.0: A Review.

Authors :
Javaid, Mohd
Haleem, Abid
Suman, Rajiv
Source :
Cognitive Robotics; 2023, Vol. 3, p71-92, 22p
Publication Year :
2023

Abstract

Digital Twin is a virtual representation of objects, processes, and systems that exist in real-time. While Digital Twin can represent digital objects, they are often used to connect the physical and digital worlds. This technology plays a vital role in fulfilling various requirements of Industry 4.0. It gives a digital image of a factory's operations, a communications network's activities, or the movement of items through a logistics system. This paper studies Digital Twin and its need in Industry 4.0. Then the process and supportive features of Digital Twin for Industry 4.0 are diagrammatically discussed, and finally, the major applications of Digital Twin for Industry 4.0 are identified. Digital Twin sophistication depends on the process or product represented and the data available. Manufacturers can learn how assets will behave in real-time, in the physical world, by putting sensors on particular assets, gathering data, creating digital duplicates, and employing machine intelligence. They can confidently make wise judgments, which helps improve company performance. Digital Twin assesses material usage to save costs, discover inefficiencies, replicate tool tracking systems, and do other things. Manufacturers construct a digital clone for specific equipment and tools, exclusive products or systems, entire procedures, or anything else they want to improve on the factory floor. Sensors and other equipment that collect real-time data on the state of the process or product collect this information, which on the other hand, must be handled and processed appropriately. It is made feasible by IoT sensors, which collect data from the physical environment and transmit it to be virtually recreated. This information comprises design and engineering details that explain the asset's shape, materials, components, and behaviour or performance. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
26672413
Volume :
3
Database :
Complementary Index
Journal :
Cognitive Robotics
Publication Type :
Academic Journal
Accession number :
172929198
Full Text :
https://doi.org/10.1016/j.cogr.2023.04.003