Technical infrastructure for digital twins - what is a digital twin and how can it be used?
A digital twin is the virtual image of material or immaterial objects. The long-term goal is to represent products and production systems along their lifecycle using digital twins, thus enabling a holistic view of individual components and entire processes. The it’s OWL project “Technical Infrastructure for Digital Twins” focuses precisely on this topic.
In order to be able to work in the manufacturing process, fictitious assumptions about the later use of the product, the operating conditions and loads and / or empirical values are often used. However, assumptions, older data sets and experiences do not always reflect reality and so it can happen that the manufacturing and product quality suffers. Industry 4.0, the beacon of hope, is expected in the long term to change the development basis for manufacturing and product assumptions in the long term.
In production, digital twins are seen as the solution for reducing uncertainties. Digital twins are the virtual image of material or immaterial objects (e.g. products, services and / or processes). They consist of data and algorithms and can be connected to real objects (e.g. machines or systems) via sensors. They are therefore ideal as a basis for development because they enable condition monitoring of machines and systems in real time and developers can use the data to simulate them risk-free without endangering operations. Digital twins can also accompany the engineering process of a machine with simulation models. Data from component manufacturers and suppliers are often "offline" in a non-interoperable form, so that the development and integration of automated support functions help to make the data usable in an application. The project "Technical Infrastructure for Digital Twins" makes use of these advantages.
The aim of the project is to develop and test a technical infrastructure for digital twins, which makes it possible to plan predictive maintenance of the systems, to use forecasts and data for future applications, and to support the engineering process. As a result, potential savings of over 50 percent can be realized.
More information about the project can be found here.
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