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Machines
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Application of Digital Twins in Industry 5.0
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Application of Digital Twins in Industry 5.0

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Industrial Systems".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 3220

Special Issue Editors

Prof. Dr. Michael Grieves

E-Mail Website
Guest Editor
Department of Engineering, University of Central Florida, Orlando, FL 32816, USA
Interests: digital twins; manufacturing technology; product lifecycle mangement
Dr. Dean Bartles

E-Mail Website
Guest Editor
Manufacturing Technology Deployment Group, Inc., Clearwater, FL 33762, USA
Interests: manufacturing technology; smart manufacturing; additive manufacturing

Special Issue Information

Dear Colleagues,

Industry 5.0 (I5.0) represents a paradigm shift where humans and manufacturing machines, utilizing digital technology capabilities, collaborate more closely. This emphasizes human-centered manufacturing. I5.0 aims to integrate the strengths of both humans and machines to enhance productivity, safety, and creativity in developing industrial processes and flawless performance in day-to-day production. Digital twins, virtual replicas of physical assets and processes, play a crucial role in I5.0 by facilitating real-time monitoring, predictive maintenance, and the optimization of production processes.

Before manufacturing facilities are built or revamped, Digital Twins enable manufacturers to virtually commission by laying out the facility and equipment, simulating various scenarios, exploring alternate production methods, and identifying potential issues before they occur. In production, Digital Twins can provide an instantaneous and simultaneous replicative view of production as it is occurring and predict potential issues and their probabilities, leading to more efficient and effective operations, increased safety, and reduced downtime.

Moreover, digital twins enhance human–machine interaction by providing AI-assisted advice as needed for operators interacting with complex equipment and processes, ultimately improving decision-making and empowering workers to contribute their expertise more effectively in collaborative environments. Overall, digital twins are instrumental in realizing the vision of Industry 5.0 by fostering synergy between humans and machines for smarter, more efficient and effective manufacturing.

Prof. Dr. Michael Grieves
Dr. Dean Bartles
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • digital twins
  • Industry 5.0
  • replicative digital twins
  • predictive maintenance
  • virtual commissioning
  • human–machine manufacturing

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Published Papers (2 papers)

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Research

21 pages, 7430 KiB  
Article
Federation in Digital Twins and Knowledge Transfer: Modeling Limitations and Enhancement
by Alexios Papacharalampopoulos, Dionysios Christopoulos, Olga Maria Karagianni and Panagiotis Stavropoulos
Machines 2024, 12(10), 701; https://doi.org/10.3390/machines12100701 - 3 Oct 2024
Viewed by 1474
Abstract
Digital twins (DTs) consist of various technologies and therefore require a wide range of data. However, many businesses often face challenges in providing sufficient data due to technical limitations or business constraints. This can result in inadequate data for training or calibrating the [...] Read more.
Digital twins (DTs) consist of various technologies and therefore require a wide range of data. However, many businesses often face challenges in providing sufficient data due to technical limitations or business constraints. This can result in inadequate data for training or calibrating the models used within a digital twin. This paper aims to explore how knowledge can be generated from federated digital twins—an approach that lies between digital twin networks and collaborative manufacturing—and how this can be used to enhance understanding for both AI systems and humans. Inspired by the concept of federated machine learning, where data and algorithms are shared across different stakeholders, this idea involves different companies collaborating through their respective DTs, a situation which can be referred to as federated twinning. As a result, the models within these DTs can be enriched with more-detailed information, leading to the creation of verified, high-fidelity models. Human involvement is also emphasized, particularly in the transfer of knowledge. This can be applied to the modeling process itself, which is the primary focus here, or to any control design aspect. Specifically, the paradigm of thermal process modeling is used to illustrate how federated digital twins can help refine underlying models. Two sequential cases are considered: the first one is used to study the type of knowledge that is required from modeling and federation; while the second one investigates the creation of a more suitable form of modeling. Full article
(This article belongs to the Special Issue Application of Digital Twins in Industry 5.0)
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12 pages, 3018 KiB  
Article
Methodical Development of a Digital Twin for an Industry Valve
by Anton Koesters, Florian Koetz, Moritz Bock, Michel Fett, Richard Breimann and Eckhard Kirchner
Machines 2024, 12(10), 674; https://doi.org/10.3390/machines12100674 - 26 Sep 2024
Cited by 1 | Viewed by 1210
Abstract
This contribution explores the development of a digital twin for industrial valves, with a focus on mitigating the costly consequences of valve malfunctions in large-scale industrial environments. Industrial valves are critical components in fluid and gas control systems where unexpected failures can lead [...] Read more.
This contribution explores the development of a digital twin for industrial valves, with a focus on mitigating the costly consequences of valve malfunctions in large-scale industrial environments. Industrial valves are critical components in fluid and gas control systems where unexpected failures can lead to significant downtime and financial loss. Digital twins as virtual replicas of physical systems offer a promising solution as they enable real-time monitoring and predictive maintenance. This paper looks at the creation of a digital twin for a specific valve type (74BS from SchuF Armaturen und Apparatebau GmbH) and considers key aspects such as model development, sensor integration and IT infrastructure. A test bench is constructed to collect the measured values to support the validation of the digital twin. The integration of sensors and the development of an IT system for data processing are also described in detail. Finally, the technically relevant frequencies are identified in an FFT. Full article
(This article belongs to the Special Issue Application of Digital Twins in Industry 5.0)
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