Asset Details
Do you wish to reserve the book?
Design and validation of a flexible framework for developing generic digital twins for Industry 4.0
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Design and validation of a flexible framework for developing generic digital twins for Industry 4.0
Do you wish to request the book?
Design and validation of a flexible framework for developing generic digital twins for Industry 4.0
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Design and validation of a flexible framework for developing generic digital twins for Industry 4.0
2025
Overview
The paper presents an innovative methodology, development and implementation of a Digital Twin prototype based on a 5 Dimensional Digital Twin architecture combined with a Service-Oriented Architecture and the
Industry 4.0 Component
concept. The proposed implementation method separates
the business logic (Digital Twin Brain)
, which enables the Digital Twin to perform its new
capabilities
, from the
domain logic
, which provides the
specific functionalities
of the Digital Twin. The specific functionalities are provided and implemented by the Asset Administration Shell Industry 4.0 Components available as stateless services, which allowed the creation of a base of shared technical functionalities among multiple Digital Twins. The methodology and architecture were validated using real data from an industrial packaging line, where the Digital Twin leverages
machine learning-based
algorithms to improve efficiency and reduce production downtime. The obtained results confirm the flexibility and scalability of the proposed architecture, which allows not only predictive maintenance but also seamless integration of additional domain functions. The proposed methodology contributes to the efficient deployment of Digital Twins in multidisciplinary environments and lays the foundation for a wide range of applications in the context of Industry 5.0.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
