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Cyber-Physical Systems for Smart Manufacturing
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Cyber-Physical Systems for Smart Manufacturing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2642

Special Issue Editors

Dr. Elvis Hozdić

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, University of Novo Mesto, Na Loko 2, 8000 Novo Mesto, Slovenia
Interests: cyber-physical production systems; mechatronic systems; additive manufacturing; reverse engineering; Industry 4.0
Prof. Dr. Zoran Jurković

E-Mail Website
Guest Editor
Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia
Interests: modeling and optimization of processes; machine tools; application of evolutionary algorithms and other natural-based algorithms; process efficiency; energy savings in production processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The ongoing industrial revolution, known as Industry 4.0, is bringing about significant changes in manufacturing processes through the adoption of cyber-physical systems (CPSs). These systems, which integrate physical manufacturing processes with intelligent computational technologies, enable dynamic adaptation, operational optimization, and improved responsiveness to changes. CPSs thus enhance the efficiency, flexibility, and resilience of manufacturing systems.

This Special Issue invites researchers and professionals to contribute articles that explore various aspects of cyber-physical systems within the context of smart manufacturing. The contributions should focus on the following areas:

  1. Development and Simulation: Papers addressing the modeling and simulation of CPSs in manufacturing, with an emphasis on the integration between cyber and physical components to improve system performance.
  2. Advanced Control Methods: Research showcasing new approaches to automation and control of manufacturing systems, including the use of artificial intelligence to enhance autonomy and process optimization.
  3. Digital Transformation of Manufacturing: Articles discussing the transformation of traditional manufacturing processes into digitally-driven systems, focusing on human–machine collaboration and the adaptation of job roles.
  4. Integration and Interoperability: Research exploring challenges and solutions in integrating CPSs with existing manufacturing structures, including the standardization of communication protocols and data exchange.
  5. Practical Applications and Validation: Case studies and experimental research that demonstrate successful CPS implementations in real-world manufacturing environments, revealing key challenges and lessons learned.
  6. Future Directions: Discussions on the latest trends in CPS development for smart manufacturing, including the development of new technologies and methods to support sustainable industrial growth.

With this Special Issue, we aim to foster the exploration and development of cyber-physical systems that will contribute to the advancement of smart manufacturing and enable a more efficient, adaptable, and sustainable industry of the future.

Dr. Elvis Hozdić
Prof. Dr. Zoran Jurković
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

  • cyber-physical systems (CPSs)
  • smart manufacturing
  • Industry 4.0
  • automation and control
  • digital and cybernetic transformation
  • simulation and modeling
  • artificial intelligence in manufacturing
  • human–machine collaboration

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Published Papers (2 papers)

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Research

19 pages, 7001 KiB  
Article
Application of Modeling and Simulation in a Self-Reprogrammable Prototype of a Manufacturing System
by Rodrigo Ferro, João Victor P. de Oliveira, Gabrielly A. Cordeiro and Robert E. C. Ordóñez
Appl. Sci. 2025, 15(6), 3298; https://doi.org/10.3390/app15063298 - 18 Mar 2025
Viewed by 276
Abstract
Shorter product life cycles and the growing demand for mass customization have led to the development of complex production systems, which are crucial for maintaining competitiveness. In this context, digital technologies and simulation tools play a fundamental role in integrating virtual and physical [...] Read more.
Shorter product life cycles and the growing demand for mass customization have led to the development of complex production systems, which are crucial for maintaining competitiveness. In this context, digital technologies and simulation tools play a fundamental role in integrating virtual and physical systems to enhance operational performance. This study presents a prototype for self-programming manufacturing systems, achieved through the integration of computer simulation and production management tools, leveraging the Digital Twin (DT) concept. To validate this approach, a prototype capable of interacting with a simulation model was developed. In the event of a failure that compromises product delivery conditions, the simulation model is activated to reprogram the production system’s operating parameters, ensuring compliance with initial production requirements and minimizing the impact of disruptions. The tests confirmed effective data exchange between the physical and virtual environments. Additionally, intentional failures were introduced in the real environment to assess system behavior. Each time a failure occurred, the simulation model generated new operating parameters, adjusting the working speed in the real environment and thereby maintaining the production system’s ability to meet its requirements. Consolidating the application of self-reprogramming. Full article
(This article belongs to the Special Issue Cyber-Physical Systems for Smart Manufacturing)
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21 pages, 8808 KiB  
Article
Prediction and Optimization of Surface Roughness and Cutting Forces in Turning Process Using ANN, SHAP Analysis, and Hybrid MCDM Method
by Mirza Pasic, Dejan Marinkovic, Dejan Lukic, Derzija Begic-Hajdarevic, Aleksandar Zivkovic, Mijodrag Milosevic and Kenan Muhamedagic
Appl. Sci. 2024, 14(23), 11386; https://doi.org/10.3390/app142311386 - 6 Dec 2024
Viewed by 1228
Abstract
As manufacturing technologies advance, the integration of artificial neural networks in machining high-hardness materials and optimization of multi-objective parameters is becoming increasingly prevalent. By employing modeling and optimization strategies during the machining of such materials, manufacturers can improve surface roughness and tool life [...] Read more.
As manufacturing technologies advance, the integration of artificial neural networks in machining high-hardness materials and optimization of multi-objective parameters is becoming increasingly prevalent. By employing modeling and optimization strategies during the machining of such materials, manufacturers can improve surface roughness and tool life while minimizing cutting time, tool vibrations, and cutting forces. In this paper, the aim was to analyze the impact of input parameters (cutting speed, feed rate, depth of cut, and insert radius) on surface roughness and cutting forces during the machining of 90MnCrV7 using feed-forward neural network models and SHAP analysis. Afterward, multi-criteria optimization was applied to determine the optimal parameter levels to achieve minimum surface roughness and cutting forces using the modified PSI-TOPSIS method. According to the SHAP analysis, the insert radius has the most significant impact on the surface roughness and passive force, while in the multi-criteria analysis, according to ANOVA results, the insert radius has the most significant impact on all considered outputs. The results show that an insert radius of 0.8 mm, a cutting speed of 260 m/min, a feed rate of 0.08 mm, and a depth of cut of 0.5 mm are the optimal combination of input parameters. Full article
(This article belongs to the Special Issue Cyber-Physical Systems for Smart Manufacturing)
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