Digital Twin

 

With the development of Industry 4.0, additive manufacturing will be widely used to produce customized components. However, it is rather time-consuming and expensive to produce components with sound structure and good mechanical properties using additive manufacturing by a trial-and-error approach. To obtain optimal process conditions, numerous experiments are needed to optimize the process variables within given machines and processes. Digital twins (DT) are defined as a digital representation of a production system or service or just an active unique product characterized by certain properties or conditions. They are the potential solution to assist in overcoming many issues in additive manufacturing, in order to improve part quality and shorten the time to qualify products. The DT system could be very helpful to understand, analyze and improve the product, service system or production. However, the development of genuine DT is still impeded due to lots of factors, such as the lack of a thorough understanding of the DT concept, framework, and development methods. Moreover, the linkage between existing brownfield systems and their data are under development. This paper aims to summarize the current status and issues in DT for additive manufacturing, in order to provide more references for subsequent research on DT systems.

Logicat Representaion of Digital Twin

Technology Types

There are three main purposes to implement a DT:

  • Product Digital Twin – to guarantee reliable design in product development and improvements;
  • Production Digital Twin – to improve production planning and manufacturing;
  • Performance Digital Twin – to capture, analyse and act on data while an asset is on operation.



Components & enablers

The DT infrastructure consists essentially out of 3 parts:

  • The Engine is responsible for operation of the central multi-user database as well as several data management functions.
  • The installed sensors and corresponding interfaces are the components obtain data and transfer within another.
  • The implemented User Interface is providing sufficient functionality such as graphical data visualization.

Advantages & field of application

The DT approach promises to improve utilization of data gathered from e.g. market participants and energy traders, generation units and power plants as well as TSO’s grid infrastructure. It can be discussed as a useful tool to affect data-driven performances in future power grids; appropriately in Power System Operation and its grid planning or in Power System Economics and its local flexibility markets.


Best practice performance

Together with Siemens AG, Fingrid introduced an electrical DT model (ELVIS) to affect their asset and operation management as well as infrastructure investment planning. Meanwhile, the DT model is used to develop several investment scenarios considering different policy frameworks. In conclusion, the data collection and verification process takes less than 20 % of the time that it used to take.

The American Electric Power (AEP) established a similar project to obtain a reduction of time and costs associated with grid and market modelling efforts manually. Furthermore, an advanced data governance foundation to support its investment strategy were implemented.


Aplications and examples.

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