Digital Twins

What is a digital twin?

A digital twin is a virtual representation of a physical object or process. It is primarily used when companies want to observe, analyze, simulate, and optimize their products or systems over their entire lifecycle. To do this, models, information, and data are continuously collected from the real object or process in real time, providing insight into its condition and behavior.

How does a digital twin work?

A digital twin has three basic components: A digital master, a digital shadow, and their intelligent linking. 

 

1. Digital master

The digital master contains all relevant models of the physical object or process. It uses appropriate data and information models to represent what is expected. Depending on the application, this may relate to the geometry, behavior, function, or other properties of products, machines, or processes.

Digital Twins are created by linking the digital shadow and digital master.

A car is used as an example. All cars of a type have been produced using the same drawings and production instructions, and have the same average maintenance cycles. We call the models, simulations, and data from which all these cars are produced »master«.

 

2. Digital shadow

The digital shadow, on the other hand, represents the actual. It consists of data that is collected over the life cycle of the system depicted. This can be operating, status or process data that is recorded by sensors, for example.

The data shows the differences between each car, even though they are identical in construction. This is due to the fact that each car has been subjected to special features during production, such as assembly variations. During operation, the car may have been used in particularly hot, cold or dry regions, requiring shorter maintenance cycles. We call this data, which represents the reality of the unique car, »shadows«.

 

3.  Intelligent linking

The real value of the digital twin comes from intelligently linking the digital master and the digital shadow.

By comparing the vehicle's master and shadow data, it is possible, for example, to predict the optimum time for maintenance or to derive important improvement potential for future product generations (feedback to design).

What types of digital twins are there and what are they used for?

 

Product twins

Product twins are digital images of real, individual objects of value. They contain data throughout their lifecycle and provide insights into product behavior and optimization potential. For example, they can be used to share data across organizations, or to plan, monitor, and optimize products at all stages of their lifecycle, including their environmental footprint and energy efficiency.

 

Machine twins, system twins and technology twins

Machine or plant twins map the current status during production. This allows us to automatically record and control the energy efficiency of systems or identify maintenance requirements at an early stage (predictive maintenance) and support maintenance with the help of context-sensitive assistance systems.

 

Process twins

Digital process twins link technical factory processes with enterprise business processes. This allows us to look at systems as a whole and gain important insights, such as production planning or business models, from a solid data foundation.

Application example: Faster development thanks to digital twins

 

Our researchers have developed a pilot system on which linked production systems can be designed cost-effectively and complex processes safely tested. The principle: A digital twin of the modular development environment ensures that the two robots are automatically configured and controlled for each assembly step.

Advantages of the solution

using the example of the automation of an assembly process for professional tools

Time and cost savings of a robot-based system compared to a traditional pilot system:

  • Pilot system in only 12 weeks instead of 12 months
  • Cost of about 175,000 € instead of over 2 million €
© Fraunhofer IPK/Ilona Glodde

Is your organization ready for the digital twin?

 

The industry has recognized the promise of digital twins as a future technology. According to a Gartner study, only 13 percent of large companies with ongoing IoT projects in major industrialized nations are currently using digital twins. However, 62 percent of respondents have such projects at least in the planning stage.

But even in companies where digital twins are in place, they are far from being used to their full potential. So far, they are mostly used as data supply systems or for security and error analysis. This is the conclusion of the »Digital Twin Readiness Assessment« study conducted by Fraunhofer IPK and msg in 2020, which found that the provision of automated value-added services and the design of autonomous or adaptive systems have only been considered in a few concepts to date.  

To fully exploit the potential of digital twins, we develop customized solutions in close cooperation with our partners and customers. We network digital twins with each other and thus enable the mapping of entire production lines with minimal time and financial expenditure. In doing so, we always keep an eye on the sustainability aspect, as the technology also offers new possibilities in this area.

How can we support you?

Here we show some examples of problems from industrial applications that can be solved with the help of digital twins. 

 

Solution

Digital twins for the circular economy

  • Closing information and material loops to promote a circular economy.
  • Design products that take into account all stages of their life cycle.
  • Reduce waste and recycle products at the end of their useful life.
 

Solution

Sustainability with the help of digital twins

  • The responsibility of product developers to design products optimally across all lifecycle phases.
  • Think ahead of the life cycle phases as part of product life cycle management.
  • Collect and evaluate a wide range of information to determine the true sustainability of a product or system.
 

Solution

Development and operation of digital product and factory twins

  • Digital twins enable current state analysis, forecasting, real-time optimization, and virtual rescheduling.
  • Support the development and operation of digital product or factory twins.
 

Solution

Models and modeling of digital twins

  • A variety of models for purpose-built digital twins: Use different types of models for development and simulation.
  • Methodological support: 8D model support for optimal design of digital twin solutions.
 

Solution

IT architectures and infrastructure for digital twins

Data consistency, heterogeneous system landscapes and cross-company collaboration place high demands on the robustness, flexibility and extensibility of the IT system architecture and the technologies used. With the Design Element Model, we offer conceptual support in the design and planning of your IT architectures and infrastructures and equip you for the use of digital twins.

Read more in the FUTUR magazine

 

FUTUR-Article

Design faster with digital twins

Our researchers have developed a pilot system on which linked production systems can be designed cost-effectively and complex processes safely tested. The principle: A digital twin of the modular development environment ensures that the two robots are automatically configured and controlled for each assembly step.

 

FUTUR-Article

All hands on deck

Today's complex products have a significant »digital component«. Key functions along their lifecycle have moved from the physical world into virtual data rooms. Entire production facilities are controlled by digital twins, and smart products record more and more data. New recycling requirements make the use of data inevitable.

 

FUTUR-Article

Digital (T)Wins

To control highly complex systems, companies are already using digital twins in many cases. Digital twins are digital replicas of a specific product, production system, or entire factory, allowing their characteristics, status, and behavior to be captured, predicted, and controlled using models, information, and data.

 

FUTUR-Article

A GPS for production

»Turn right in 500 meters.« When driving, we like to rely on digital assistance in the form of a navigation system. Wouldn't it be nice if such simple instructions could also be given in complex production processes? Fraunhofer IPK develops solutions for digital assistance systems  that provide users with individual and customizable support.

 

FUTUR-Article

Context is everything

In the area of maintenance, repair and overhaul (MRO), mobile assistance applications are in vogue because it is especially important to document the steps taken to maintain machines and systems so that other workers can understand what work has been done. AI makes this more flexible - and more predictive. 

Publications about Digital Twins

Jahr
Year
Titel/Autor:in
Title/Author
Publikationstyp
Publication Type
2024 Energieeffizienzsteigerung mit IoT-basiertem Monitoringsystem
Uhlmann, Eckart; Polte, Julian; Geisert, Claudio
Zeitschriftenaufsatz
Journal Article
2024 Digital Twins within the Circular Economy: Literature Review and Concept Presentation
Mügge, Janine; Seegrün, Anne; Hoyer, Tessa-Katharina; Riedelsheimer, Theresa; Lindow, Kai
Zeitschriftenaufsatz
Journal Article
2024 Was macht einen digitalen Zwilling aus?
Lindow, Kai; Tanrikulu, Cansu
Zeitschriftenaufsatz
Journal Article
2024 Developing Digital Twins for energy efficiency in the production phase of products
Wehking, Sebastian; Riedelsheimer, Theresa; Tanrikulu, Cansu; Lindow, Kai
Zeitschriftenaufsatz
Journal Article
2024 Sustainable Product Lifecycle Management - Implementation of a Digital Twin of a Biologically Transformed Product-Ecosystem
Wehking, Sebastian; Seegrün, Anne; Riedelsheimer, Theresa; Lindow, Kai
Zeitschriftenaufsatz
Journal Article
2024 Developing Digital Twins for Smart Product-Service Systems:A Methodical Approach Demonstrated with a Fuel Cell Use Case
Kenfack Zangue, Renaud; Gogineni, Sonika; Geisert, Claudio
Konferenzbeitrag
Conference Paper
2024 Concept for a modular system model for energy-efficiency monitoring of factory supply systems
Sigg, Stefan; Thiele, Gregor; Trapp, Marvin; Krüger, Jörg
Konferenzbeitrag
Conference Paper
2024 Value co-creation through digital twins in decentralized data ecosystems
Tanrikulu, Cansu; Berg, Henning; Konietzko, Erik Paul; Rosa Cencic, Maiara; Lindow, Kai
Konferenzbeitrag
Conference Paper
2023 Sustainable product lifecycle management with Digital Twins: A systematic literature review
Seegrün, Anne; Kruschke, Thomas; Mügge, Janine; Hardinghaus, Louis; Knauf, Tobias; Riedelsheimer, Theresa; Lindow, Kai
Zeitschriftenaufsatz
Journal Article
2023 Digital Twin for Circular Economy
Mügge, Janine; Riedelsheimer, Theresa; Lindow, Kai
Konferenzbeitrag
Conference Paper
2023 End-of-life decision support to enable circular economy in the automotive industry based on digital twin data
Mügge, Janine; Hahn, Inka Rebekka; Riedelsheimer, Theresa; Chatzis, Johannes; Boes, Joachim
Zeitschriftenaufsatz
Journal Article
2023 Implementing digital twins in existing infrastructures
Lünnemann, Pascal; Lindow, Kai; Goßlau, Leo
Zeitschriftenaufsatz
Journal Article
2023 Smarte Überwachung elektrischer Großantriebe
Geisert, Claudio; Polte, Julian; Uhlmann, Eckart; Rauch, Hartmut; Brach, Karsten
Zeitschriftenaufsatz
Journal Article
2023 Blue Print Plant Model. Ein Modell zur Unterstützung des Fabrikplanungsprozesses
Lange, Annika; Ihnen, Deike Magret; Knothe, Thomas
Zeitschriftenaufsatz
Journal Article
2023 Empowering End-of-Life Vehicle Decision Making with Cross-Company Data Exchange and Data Sovereignty via Catena-X
Mügge, Janine; Große Erdmann, Julian; Riedelsheimer, Theresa; Manoury, Marvin Michael; Smolka, Sophie Odette; Wichmann, Sabine; Lindow, Kai
Zeitschriftenaufsatz
Journal Article
2023 The Digital Twin for Operations, Maintenance, Repair and Overhaul
Lünnemann, Pascal; Fresemann, Carina; Richter, Friederike
Aufsatz in Buch
Book Article
2023 Smart Maintenance - Was ist das und was kann es?
Geisert, Claudio
Zeitschriftenaufsatz
Journal Article
2023 Digital Twins for Sustainability in the Context of Biological Transformation
Seegrün, Anne; Mügge, Janine; Riedelsheimer, Theresa; Lindow, Kai
Konferenzbeitrag
Conference Paper
2023 Project-Based Learning in Engineering Education - Developing Digital Twins in a Case Study
Hagedorn, Lisa; Riedelsheimer, Theresa; Stark, Rainer
Zeitschriftenaufsatz
Journal Article
2023 F5G OpenLab: Enabling Twin Transition through Ubiquitous Fiber Connectivity
Balanici, Mihail; Shariati, Mohammad Behnam; Safari, Pooyan; Chojecki, Paul; Chemnitz, Philipp Axel Moritz; Przewozny, David; Fischer, Johannes; Freund, Ronald
Konferenzbeitrag
Conference Paper
2022 Resource efficient production of car body parts - implementation of digital twins across process chains
Weber, Joshua; Lemke, Josefine; Sunderkoetter, Christina; Haase, Patrick; Hoefemann, Matthias; Joos, Paul; Merklein, Marion
Vortrag
Presentation
2022 A cognitive assistance system with augmented reality for manual repair tasks with high variability based on the digital twin
Eversberg, Leon; Ebrahimi, Puya; Pape, Martin; Lambrecht, Jens
Zeitschriftenaufsatz
Journal Article
2022 Harmonization of Heterogeneous Asset Administration Shells
Koutrakis, Nikolaos-Stefanos; Gowtham, Varun; Pilchau, W.B.P. von; Jung, T.J.; Polte, Julian; Hähner, J.; Corici, Marius-Iulian; Magedanz, Thomas; Uhlmann, Eckart
Zeitschriftenaufsatz
Journal Article
2022 The Use of Digital Twins to Overcome Semantic Barriers in Hyperconnected Ecosystems for Industry
Jäkel, Frank-Walter; Gering, Patrick; Knothe, Thomas
Konferenzbeitrag
Conference Paper
2022 Application of Uncertainty-Aware Sensor Fusion in Physical Sensor Networks
Gruber, Maximilian; Pilar von Pilchau, Wenzel; Gowtham, Varun; Koutrakis, Nikolaos-Stefanos; Schönborn, Nicolas; Eichstädt, Sascha; Hähner, Jörg; Corici, Marius-Iulian; Magedanz, Thomas; Polte, Julian; Uhlmann, Eckart
Konferenzbeitrag
Conference Paper
2022 Digital Twins for Circular Economy - Enabling Decision Support for R-Strategies
Mügge, Janine; Hahn, Inka Rebekka; Riedelsheimer, Theresa; Chatzis, Johannes
Zeitschriftenaufsatz
Journal Article
2021 Production in the loop - the interoperability of digital twins of the product and the production system
Vogt, Anna; Schmidt, Philipp Heiner; Mayer, Sebastian; Stark, Rainer
Zeitschriftenaufsatz
Journal Article
2021 Progress for Life Cycle Sustainability Assessment by Means of Digital Lifecycle Twins - A Taxonomy
Riedelsheimer, Theresa; Neugebauer, Sabrina; Lindow, Kai
Aufsatz in Buch
Book Article
2021 Agiles Modellieren von Servicetätigkeiten
Uhlmann, Eckart; Bösing, Manuel; Polte, Julian; Kirsch, Lucas; Altmann, Ian; Emmerling, Roman
Zeitschriftenaufsatz
Journal Article
2021 Holistic Concept towards a Reference Architecture Model for Predictive Maintenance
Uhlmann, Eckart; Polte, Julian; Koutrakis, Nikolaos-Stefanos
Zeitschriftenaufsatz
Journal Article
2021 Methodology to develop Digital Twins for energy efficient customizable IoT-Products
Riedelsheimer, Theresa; Gogineni, Sonika; Stark, Rainer
Zeitschriftenaufsatz
Journal Article
2021 Enabling automated engineering's project progress measurement by using data flow models and digital twins
Ebel, Helena; Riedelsheimer, Theresa; Stark, Rainer
Zeitschriftenaufsatz
Journal Article
2021 Concept and Architecture for Information Exchange between Digital Twins of the Product (CPS) and the Production System (CPPS)
Vogt, Anna; Müller, Ralph Klaus; Kampa, Thomas; Stark, Rainer; Großmann, Daniel
Zeitschriftenaufsatz
Journal Article
2021 Sensor integration in hybrid additive manufactured parts for real-time monitoring in turbine operations
Uhlmann, Eckart; Polte, Julian; Kersting, Robert; Brunner-Schwer, Christian; Neuwald, Tobias
Konferenzbeitrag
Conference Paper
2020 User centered development of a digital twin concept with focus on sustainability in the clothing industry
Riedelsheimer, Theresa; Dorfhuber, Lisa; Stark, Rainer
Zeitschriftenaufsatz
Journal Article
2020 Digital Twin Readiness Assessment
Riedelsheimer, Theresa; Lünnemann, Pascal; Wehking, Sebastian; Dorfhuber, Lisa
Studie
Study
2019 Digitaler Zwilling für Smart Services
Exner, Konrad; Preidel, Maurice; Gogineni, Sonika; Stark, Rainer
Zeitschriftenaufsatz
Journal Article
2019 Digital Twin
Stark, Rainer; Damerau, Thomas
Aufsatz in Buch
Book Article
2019 Development capabilities for smart products
Tomiyama, T.; Lutters, E.; Stark, R.; Abramovici, M.
Zeitschriftenaufsatz
Journal Article
2019 Use of Digital Twins in Additive Manufacturing Development and Production
Bergmann, André; Lindow, Kai
Konferenzbeitrag
Conference Paper
2019 Development and operation of Digital Twins for technical systems and services
Stark, Rainer; Fresemann, Carina; Lindow, Kai
Zeitschriftenaufsatz
Journal Article
2019 Reale Daten für Simulationen im digitalen Zwilling - Untersuchung zur Aufnahme von Profinet-Daten und deren Wiedergabe in komplexen Simulationsumgebungen
Chemnitz, Philipp Axel Moritz; Heimann, Oliver; Vick, Axel
Zeitschriftenaufsatz
Journal Article
2019 Reale Daten für Simulationen im digitalen Zwilling
Chemnitz, Moritz; Heimann, Oliver; Vick, A.
Zeitschriftenaufsatz
Journal Article
2019 Implementation of an Energy Metering System for Smart Production
Halstenberg, Friedrich A.; Lindow, Kai; Stark, Rainer
Aufsatz in Buch
Book Article
Diese Liste ist ein Auszug aus der Publikationsplattform Fraunhofer-Publica

This list has been generated from the publication platform Fraunhofer-Publica