The digital twin in the smart factory

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Headings…
The digital twin in the smart factory
A digital twin is a near-real-time representation of the world’s physical a
Case studies of the digital twin
What role does the Digital Twin play in Industrie 4.0

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The introduction of cognitive and intelligent processes has opened up the possibly of a wide range of productivity enhancements which are driving the digital transformation of the manufacturing sector.
This has in turn brought up its own challenges, as manufacturing processes become ever more digital and automated.
One method that will be invaluable in facing these challenges, both in the present and the future, is the concept of the Digital Twin.
A Digital Twin is a near-real-time representation of the physical attributes of a factory, product, or process.
Through the connection of objects generating real-time data, a digital footprint of a product, from its design and development stages through to the end of its production cycle, can be developed.
A well-constructed Digital Twin can facilitate the simulation of possible scenarios and predict possible outcomes, allowing one to try new strategies without impacting production.
A Digital Twin can add further value through facilitating the remote performance of assistance and other service tasks.
Case studies of the digital twin
We are already seeing Digital Twins being applied in a wide variety of manufacturing industries.
For example, in the automotive industry, Digital Twins can be used for a selection of car models, generations and configurations to model the number and type of microcontrollers, sensors and actuators as well as the installed software components.
Using a digital twin, manufacturers can automate the process of data collection depending on the required platform to harmonise the various data models.
What role does the Digital Twin play in Industrie 4.0
Now we have the physical and digital systems converging; with a Digital Twin able to accurately represent a physical system.
In this context, Digital Twins can be used to evaluate production decisions prior to their implementation and to visualise performance

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