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Advances in Smart Manufacturing: Integrating AI, Digital Twins, and Edge Computing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 432

Special Issue Editors


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Guest Editor
Department for Manufacturing Technologies and Systems, Faculty of Mechanical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: smart factories; smart manufacturing; digital twins and digital agents; production logistics; AR; VR; 5G; distributed systems and edge computing; AI-based algorithms; concepts for decision and prediction

E-Mail Website
Guest Editor
Department for Manufacturing Technologies and Systems, Faculty of Mechanical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: smart handling and assembly; pneumatics; hydraulics; piezo technology in manufacturing; smart factory concepts; modelling and simulation

Special Issue Information

Dear Colleagues,

Smart manufacturing lies at the heart of modern industrial transformation, where technologies such as artificial intelligence (AI), digital twins, and edge computing enable more dynamic, agile, flexible, and intelligent production processes. This Special Issue explores the latest advancements in these key technologies and their impact on the evolution of future smart factories, as well as principles of transforming the existing factories towards smart factories.

The central focus of the Special Issue is the integration of AI and digital twins to enhance the modelling, real-time simulation, and optimization of industrial systems and processes including production logistics. Applications of edge computing and 5G networks in distributed production networks, which enable decentralized and rapid data processing, and their role in ensuring high responsiveness and flexibility in manufacturing will also be addressed.

The Special Issue emphasizes interdisciplinary research on theoretical and practical challenges, including questions of security, scalability, and sustainable implementation. It also includes case studies showcasing the application of these technologies in real-world industrial environments and discussions on future research directions and innovations.

By encompassing a wide range of topics, this Special Issue aims to contribute to the development of intelligent, sustainable, and highly adaptable industrial ecosystems that will reshape modern manufacturing.

Prof. Dr. Niko Herakovič
Dr. Marko Šimic
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • smart manufacturing
  • flexible and agile manufacturing
  • production logistics
  • artificial intelligence
  • digital twins
  • edge computing
  • 5G networks
  • real-time simulations and optimizations
  • sustainable manufacturing

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Published Papers (1 paper)

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Research

22 pages, 3950 KiB  
Article
A Deep Reinforcement Learning-Based Concurrency Control of Federated Digital Twin for Software-Defined Manufacturing Systems
by Rubab Anwar, Jin-Woo Kwon and Won-Tae Kim
Appl. Sci. 2025, 15(15), 8245; https://doi.org/10.3390/app15158245 - 24 Jul 2025
Viewed by 131
Abstract
Modern manufacturing demands real-time, scalable coordination that legacy manufacturing management systems cannot provide. Digital transformation encompasses the entire manufacturing infrastructure, which can be represented by digital twins for facilitating efficient monitoring, prediction, and optimization of factory operations. A Federated Digital Twin (FDT) emerges [...] Read more.
Modern manufacturing demands real-time, scalable coordination that legacy manufacturing management systems cannot provide. Digital transformation encompasses the entire manufacturing infrastructure, which can be represented by digital twins for facilitating efficient monitoring, prediction, and optimization of factory operations. A Federated Digital Twin (FDT) emerges by combining heterogeneous digital twins, enabling real-time collaboration, data sharing, and collective decision-making. However, deploying FDTs introduces new concurrency control challenges, such as priority inversion and synchronization failures, which can potentially cause process delays, missed deadlines, and reduced customer satisfaction. Traditional concurrency control approaches in the computing domain, due to their reliance on static priority assignments and centralized control, are inadequate for managing dynamic, real-time conflicts effectively in real production lines. To address these challenges, this study proposes a novel concurrency control framework combining Deep Reinforcement Learning with the Priority Ceiling Protocol. Using SimPy-based discrete-event simulations, which accurately model the asynchronous nature of FDT interactions, the proposed approach adaptively optimizes resource allocation and effectively mitigates priority inversion. The results demonstrate that against the rule-based PCP controller, our hybrid DRLCC enhances completion time maximum of 24.27% to a minimum of 1.51%, urgent-job delay maximum of 6.65% and a minimum of 2.18%, while preserving lower-priority inversions. Full article
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