Innovations in Digital Twin Technologies for Industry 5.0
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".
Deadline for manuscript submissions: 30 September 2026 | Viewed by 29
Special Issue Editors
Interests: digital twins; Industry 5.0; cyber physical systems
Special Issue Information
Dear Colleagues,
The development and implementation of Digital Twins are central concerns for realizing the vision of Industry 5.0, which aims to create more resilient, sustainable, and human-centric production systems. In this respect, recent years have seen a significant expansion in the capabilities and applications of Digital Twins within complex industrial environments. This evolution has been positively driven by factors such as the following: advances in sensor technology, IoT/IIoT, and connectivity; progress in cloud and edge computing platforms; and the development of advanced tools for artificial intelligence, modeling, and simulation. All these factors converge to shape current trends in the creation, operation, and exploitation of Digital Twins for the industry of the future.
We are pleased to invite you to contribute to this Special Issue. We aim to collate interdisciplinary contributions focused on innovations in Digital Twin technologies that serve as key enablers for Industry 5.0. We hope that you will be motivated to conduct and subsequently submit original research.
This Special Issue aims to cover topics related to the design, implementation, and application of Digital Twins, including theoretical studies, practical solutions, and case studies applicable to cyber-physical systems, smart production lines, collaborative supply chains, human–robot work environments, and more.
For this Special Issue, original research articles and reviews are welcome. Research areas may include, but are not limited to, the following
- Cognitive and scalable architectures for Digital Twins in Industry 5.0.
- Integration of generative AI, physics-based simulation, and data models in Digital Twins.
- Frameworks for human–Digital Twin collaboration and augmented decision-making.
- Digital Twins for mass customization and value co-creation.
- Implementation of Digital Twins for sustainability and the circular economy.
- Whole-system Digital Twins for supply chain resilience.
- Interoperability, standardization, and security in Digital Twin ecosystems.
- Digital Twins for prescriptive maintenance and remaining useful life prediction.
- Integration of Digital Twins with edge computing and the industrial metaverse.
- Practical case studies and lessons learned from Digital Twin implementation.
- Ethical, social, and governance aspects of Digital Twin development.
Dr. Andrés Redchuk
Dr. Federico Walas Mateo
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 250 words) can be sent to the Editorial Office for assessment.
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
- cognitive digital twin
- real-time simulation
- data-driven process optimization
- Industry 5.0
- smart manufacturing
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