Digital twin

Digital representation of a real system

A digital twin represents a real object in the digital world. They may be tangible or intangible objects. The digital twins are built from data and algorithms and can be linked to the real world through sensors. Digital twins are the basis for Industry 4.0 processes.

Prototypes and demonstrators available

Cross-industry deployment

Suitable for SMEs?

State of play

The rapid development of digital twins has shown an increasing trend in industrial deployment since 2017. Large companies in particular have already introduced or are planning to implement digital twins in their companies. Small and medium-sized enterprises are also growing.

Current challenges in the implementation of digital twins can be found on both the technical and socio-technical sides. Secondary development issues, such as artificial intelligence, IT security or the Model Based System Engineering, on which the success of digital twins have not yet been built and embedded in all companies.

Technology and deployment

Description of technology

Elements of a digital twin

Possible use scenarios

The comprehensive representation of complex systems in all areas of the product lifecycle requires a distinction between digital twins in ‘product twins’, ‘production twins’ and ‘service twins’. The mapping of real objects based on collected data allows for a wide range of applications.

One field of application is the continuous adaptation and development of products through the use of real data. In addition, digital twins are already being used in the service sector. Data collected is used to describe the actual state of the system. Using simulation models or predictive algorithms, maintenance can be carried out predictively rather than as usual.

Gradual introduction

Step 1: Design and production phase

Digital twins are user-specific and need to be tailored to the company and the question. An analysis of current information, document and communication flows, both internally and with cooperating companies, makes it possible to: Baseline define in more detail.

With regard to the Objective several issues need to be addressed:

—Which digital twin will be implemented (product, production, service twin)?

—What task should it perform (Visualisation platform, simulation model, etc.)?

—What data are relevant for this?

Step 2: Operational phase

Each digital twin is based on an IIoT platform that can feed data collected from different product life-cycle domains. An interface-open system architecture must be in place.

Thus, the digital twin can be continuously ‘fed’ and usable with relevant data.

Step 3: Enlargement phase

Knowledge can feed into the further development of a product or process, but also into the further development of the digital twin. An extension or adjustment of the twin due to the new starting situation can be carried out within an iterative process.

Opportunities for SMEs

Time savings in the development and production process

Optimised inter-departmental and inter-company cooperation

Predictive maintenance by forecasting

Use of real field data for product development

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