Illustrating the benefits of efficient creation and adaption of behavior models in intelligent Digital Twins over the machine life cycle
Daniel Dittler, Valentin Stegmaier, Nasser Jazdi, Michael Weyrich

TL;DR
This paper explores efficient methods for creating and adapting behavior models in Digital Twins to enhance their practical application across the entire machine life cycle, supported by real-world use cases.
Contribution
It introduces low-effort creation and automatic adaptation approaches for behavior models in Digital Twins, bridging the gap between research and industry applications.
Findings
Demonstrates benefits of behavior models in various life cycle phases
Proposes methods for cost-effective creation and adaptation
Provides real-world use cases illustrating practical advantages
Abstract
The concept of the Digital Twin, which in the context of this paper is the virtual representation of a production system or its components, can be used as a "digital playground" to master the increasing complexity of these assets. One of the central subcomponents of the Digital Twin are behavior models that can enable benefits over the entire machine life cycle. However, the creation, adaption and use of behavior models throughout the machine life cycle is very time-consuming, which is why approaches to improve the cost-benefit ratio are needed. Furthermore, there is a lack of specific use cases that illustrate the application and added benefit of behavior models over the machine life cycle, which is why the universal application of behavior models in industry is still lacking compared to research. This paper first presents the fundamentals, challenges and related work on Digital Twins…
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Taxonomy
MethodsFocus
