Abstract

AbstractA Digital Twin is a virtual representation of a physical asset. It reflects the current state of that machine through a model and the data as observed by sensors in the real machine; and enables effective and efficient interaction with the machine, i.e. for monitoring and control purposes. The Digital Twin facilitates the collection of data, as well as its analysis and visualization through its user interfaces, i.e. GUIs such as screens or Mixed Reality that provide intuitive access to the data and facilitates its manipulation. Embedded in Virtual Testbeds the Digital Twin becomes an “Experimentable Digital Twin” (EDT), in which experiments can be performed and the different outcomes can be compared or evaluated. The intuitive representation of the assets allows the experts to interact with the twin, without highly detailed knowledge in computer science. The digital twin observes, records, and benchmarks experiments performed by the operator. This way the operator’s knowledge becomes digitized and thus preserved as an abstract representation of data, formulas, and models inside the digital twin. By introducing the Digital Twin into the processes carried out by different operators (not only the initially observed expert), formerly intuitive decision-making processes of the operators are enhanced based on empirical data. As a result, the Digital Twin serves as an assistance system that can guide future operators and the outcomes of the experiments become reproducible. The specific representations of interactions and outcomes also facilitate collaboration between the machine operators and other stakeholders by providing different operators a common “perspective”.

Highlights

  • With the advance of Industry 4.0, there is a shift from mass production to innovative, specialized products with small batch sizes

  • Considering the activities in Mohammad and Al Saiyd [6], we propose to use the Digital Twin as a means of knowledge representation and to store the knowledge base

  • The Digital Twin is a virtual model of an asset that can be used to store and visualize machine data

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Summary

Introduction

With the advance of Industry 4.0, there is a shift from mass production to innovative, specialized products with small batch sizes. The new settings need to be tested before the actual production can begin further increasing time and cost requirements. On older machines, which are still in use in some industry sectors, machine parameters need to be adjusted manually for each product, limiting the possibility of batch size 1 and driving costs up for small batch sizes. In some disciplines belonging to the traditional mechanical engineering sector (as textile mechanical engineering), the success of the outcome is depending on the skills of the operator and his experience in setting special machine parameters. Effective production is only possible with experienced, skilled personnel This knowledge is not measurable and not transformable. Due to the limited use of digitalization in some industry sectors, the training takes long, and even skilled workers need time to learn to use an individual machine independently.

What is a Digital Twin?
What is an Experimentable Digital Twin?
Digital Twin as a Mediator
What is Knowledge Management?
How to Conserve Knowledge with a Digital Twin
Use Case
Conclusion

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