Cyber-Physical Systems in Intelligent Manufacturing

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

Deadline for manuscript submissions: 15 November 2024 | Viewed by 5244

Special Issue Editors


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Guest Editor
Department of Mechanical Engineering and Aeronautics, University of Patras, Rio Patras 26504, Greece
Interests: robotic systems; automation; augmented, mixed, and virtual reality in manufacturing; manufacturing process modeling; cloud technologies; Internet of Things (IoT); digital twin; 5G; artificial intelligence; product–service systems (PSS); Industry 4.0; Industry 5.0
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Guest Editor Assistant
Laboratory for Manufacturing Systems and Automation, Department of Mechanical and Aeronautics Engineering, University of Patras, 26504 Rio Patras, Greece
Interests: robotic systems; automation; augmented, mixed, and virtual reality in manufacturing; manufacturing process modeling; cloud technologies; Internet of Things (IoT); digital twin; 5G; artificial intelligence; product-service systems (PSS)

Special Issue Information

Dear Colleagues,

The recent advances in digital and information and communication technologies (ICT), such as industrial Internet of Things (IIoT), big data analytics, artificial intelligence (AI), extended reality (XR), cyber-physical systems (CPS), and digital twins, facilitated the development of a new manufacturing paradigm, known as intelligent manufacturing. In view of the upcoming Industry 5.0, human operators are becoming the center of attention. With the integration of AI, CPS are transformed in order to enable human–cyber integration and the generation of human–cyber-physical systems (HCPS), in an attempt improve working environments by further reducing uncertainties due to the presence of human operators. Furthermore, with the integration of HCPS, new opportunities arise for the utilization and synthesis of heterogeneous data derived from cross-domains and cross-layers. Thus, intelligent manufacturing systems will be able to percept, analyze, and assist engineers in decision-making and control processes. Beyond that, due to the nature of AI frameworks, intelligent manufacturing systems are also capable of learning, adapting, and generating knowledge.

Therefore, this Special Issue will gather research studies which explore the opportunities and challenges arising from the integration of cutting-edge technologies in modern manufacturing and production systems and networks, as well as studies which discuss the design, development, and implementation of HCPS frameworks for keeping human-in-the-loop.

Prof. Dr. Dimitris Mourtzis
Guest Editor

John Angelopoulos
Guest Editor Assistant

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

  • cyber-physical systems
  • human–cyber-physical systems
  • intelligent manufacturing
  • artificial intelligence
  • big data
  • industry 5.0

Published Papers (3 papers)

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Research

55 pages, 4692 KiB  
Article
Towards DevOps for Cyber-Physical Systems (CPSs): Resilient Self-Adaptive Software for Sustainable Human-Centric Smart CPS Facilitated by Digital Twins
by Jürgen Dobaj, Andreas Riel, Georg Macher and Markus Egretzberger
Machines 2023, 11(10), 973; https://doi.org/10.3390/machines11100973 - 19 Oct 2023
Viewed by 1575
Abstract
The Industrial Revolution drives the digitization of society and industry, entailing Cyber-Physical Systems (CPSs) that form ecosystems where system owners and third parties share responsibilities within and across industry domains. Such ecosystems demand smart CPSs that continuously align their architecture and governance to [...] Read more.
The Industrial Revolution drives the digitization of society and industry, entailing Cyber-Physical Systems (CPSs) that form ecosystems where system owners and third parties share responsibilities within and across industry domains. Such ecosystems demand smart CPSs that continuously align their architecture and governance to the concerns of various stakeholders, including developers, operators, and users. In order to satisfy short- and long-term stakeholder concerns in a continuously evolving operational context, this work proposes self-adaptive software models that promote DevOps for smart CPS. Our architectural approach extends to the embedded system layer and utilizes embedded and interconnected Digital Twins to manage change effectively. Experiments conducted on industrial embedded control units demonstrate the approach’s effectiveness in achieving sub-millisecond real-time closed-loop control of CPS assets and the simultaneous high-fidelity twinning (i.e., monitoring) of asset states. In addition, the experiments show practical support for the adaptation and evolution of CPS through the dynamic reconfiguring and updating of real-time control services and communication links without downtime. The evaluation results conclude that, in particular, the embedded Digital Twins can enhance CPS smartness by providing service-oriented access to CPS data, monitoring, adaptation, and control capabilities. Furthermore, the embedded Digital Twins can facilitate the seamless integration of these capabilities into current and future industrial service ecosystems. At the same time, these capabilities contribute to implementing emerging industrial services such as remote asset monitoring, commissioning, and maintenance. Full article
(This article belongs to the Special Issue Cyber-Physical Systems in Intelligent Manufacturing)
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20 pages, 4346 KiB  
Article
Reactive Power Optimization Based on the Application of an Improved Particle Swarm Optimization Algorithm
by Dimitris Mourtzis and John Angelopoulos
Machines 2023, 11(7), 724; https://doi.org/10.3390/machines11070724 - 09 Jul 2023
Cited by 1 | Viewed by 1168
Abstract
Climate change, improved energy efficiency, and access to contemporary energy services are among the key topics investigated globally. The effect of these transitions has been amplified by increased digitization and digitalization, as well as the establishment of reliable information and communication infrastructures, resulting [...] Read more.
Climate change, improved energy efficiency, and access to contemporary energy services are among the key topics investigated globally. The effect of these transitions has been amplified by increased digitization and digitalization, as well as the establishment of reliable information and communication infrastructures, resulting in the creation of smart grids (SGs). A crucial aspect in optimizing energy production and distribution is reactive power optimization, which involves the utilization of algorithms such as particle swarm optimization (PSO). However, PSO algorithms can suffer from premature convergence and being trapped in local optima. Therefore, in this research the design and development of an improved PSO algorithm for minimization of power loss in the context of SGs is the key contribution. For digital experimentation and benchmarking of the proposed framework, the IEEE 30-bus standardized model is utilized, which has indicated that an improvement of approximately 11% compared to conventional PSO algorithms can be achieved. Full article
(This article belongs to the Special Issue Cyber-Physical Systems in Intelligent Manufacturing)
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35 pages, 8141 KiB  
Article
Communication Safety of Cybernetic Systems in a Smart Factory Environment
by Igor Halenar, Lenka Halenarova and Pavol Tanuska
Machines 2023, 11(3), 379; https://doi.org/10.3390/machines11030379 - 12 Mar 2023
Cited by 2 | Viewed by 1621
Abstract
The aim of this contribution is to propose the architecture for a layered design of the production system. This proposal uses the IEC 62443 norm, including the Defense-in-Depth strategy and proven technical principles applicable in a Smart Factory with a focus on communication [...] Read more.
The aim of this contribution is to propose the architecture for a layered design of the production system. This proposal uses the IEC 62443 norm, including the Defense-in-Depth strategy and proven technical principles applicable in a Smart Factory with a focus on communication security. Firstly, the identification of communication forms and trends in the Smart Factory environment was identified considering the spectrum of communication protocols used within various types of automation structures used in modern production facilities. The next part of the work deals with the definition of wired and wireless forms of data transfers in production systems including their advantages and disadvantages from the view of cybernetic safety and threads in communication systems, together with the description of norms from the field of security of communication systems applicable in the industrial environment. The core of this work is the proposal of the methodology to secure the Smart Factory production system in the Industry 4.0 environment. The proposal defines important implementation steps together with a summarization of the generally applicable basic principles suitable for the process of securing a Cyber production system or Smart Factory in an industrial environment, including the example of an Iptables firewall configuration within the OPC UA communication protocol and the real example of a Smart Factory production system segmentation. Full article
(This article belongs to the Special Issue Cyber-Physical Systems in Intelligent Manufacturing)
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