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Advancement in Smart Manufacturing and Industry 4.0
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Advancement in Smart Manufacturing and Industry 4.0

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

Deadline for manuscript submissions: 20 March 2025 | Viewed by 13691

Special Issue Editors

Dr. Slavko Rakic

E-Mail Website
Guest Editor
Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
Interests: digital product–service systems; digital servitization; Industry 4.0; smart manufacturing; Industry 5.0; research projects
Dr. Ugljesa Marjanovic

E-Mail Website
Guest Editor
Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
Interests: innovation and technology; digital servitization; Industry 4.0; digital business models; research projects
Special Issues, Collections and Topics in MDPI journals
Dr. Nenad Medic

E-Mail Website
Guest Editor
Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia
Interests: Industry 4.0; decision making; operations management; technology innovation; smart manufacturing

Special Issue Information

Dear Colleagues,

This Special Issue, "Advancements in Smart Manufacturing and Industry 4.0", presents a comprehensive exploration of the rapidly evolving manufacturing landscape in the 21st century. As Guest Editors, it is our privilege to invite authors to submit articles that delve into the transformative potential of innovative technologies and Industry 4.0 principles across various industrial domains.

Articles may cover a diverse array of issues, including but not limited to:

  • Industry 4.0 and Industry 5.0;
  • Digital twins and simulation;
  • IoT and connectivity;
  • Digital product–service systems;
  • Sustainability and green manufacturing.

Through these contributions, this Special Issue will underscore the immense potential of smart manufacturing and Industry 4.0 to increase efficiency, reduce costs, and drive innovation. It also aims to highlight the need for interdisciplinary collaboration and a holistic approach to embrace these technologies effectively.

Dr. Slavko Rakic
Dr. Ugljesa Marjanovic
Dr. Nenad Medic
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

  • Industry 4.0
  • Industry 5.0
  • digital product–service systems
  • smart manufacturing
  • sustainability
  • Internet of Things

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Published Papers (7 papers)

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Research

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16 pages, 4551 KiB  
Article
Artificial Intelligence Model Used for Optimizing Abrasive Water Jet Machining Parameters to Minimize Delamination in Carbon Fiber-Reinforced Polymer
by Ioan Alexandru Popan, Vlad I. Bocăneț, Selver Softic, Alina Ioana Popan, Nicolae Panc and Nicolae Balc
Appl. Sci. 2024, 14(18), 8512; https://doi.org/10.3390/app14188512 - 21 Sep 2024
Viewed by 793
Abstract
This study introduces an artificial neural network (ANN) model for optimizing process parameters to reduce the chances of delamination in carbon fiber-reinforced polymer (CFRP) materials during abrasive water jet (AWJ) piercing. AWJ is a proper method for cutting CFRP. The initial step in [...] Read more.
This study introduces an artificial neural network (ANN) model for optimizing process parameters to reduce the chances of delamination in carbon fiber-reinforced polymer (CFRP) materials during abrasive water jet (AWJ) piercing. AWJ is a proper method for cutting CFRP. The initial step in this process is AWJ piercing, which creates entry holes in the material to facilitate further cutting operations. However, AWJ piercing is particularly challenging due to the high energy applied to the material. If it is not properly controlled, this high-energy impact can cause material delamination. Avoiding CFRP delamination is a critical aspect when expensive parts are processed with AWJ, particularly in the aerospace and automotive industries. This can compromise the CFRP workpiece, and this induces extra costs for rework. The ANN model was trained using backpropagation to predict delamination. It features a feed-forward architecture that balances model complexity and performance. Validation showed that the ANN model effectively predicted optimal process parameters, eliminating delamination in machined CFRP parts. This study underscores the potential of ANNs in enhancing AWJ piercing processes and provides a robust and reliable method of improving the quality of CFRP parts. Full article
(This article belongs to the Special Issue Advancement in Smart Manufacturing and Industry 4.0)
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17 pages, 1747 KiB  
Article
HumanEnerg Hotspot: Conceptual Design of an Agile Toolkit for Human Energy Reinforcement in Industry 5.0
by Ifeoma Chukwunonso Onyemelukwe, José Antonio Vasconcelos Ferreira, Ana Luísa Ramos and Inês Direito
Appl. Sci. 2024, 14(18), 8371; https://doi.org/10.3390/app14188371 - 18 Sep 2024
Viewed by 622
Abstract
This paper presents the conceptual design of the HumanEnerg Hotspot, an agile toolkit aimed at addressing the human energy crisis in the context of Industry 5.0. The toolkit has been developed using a blend of Design Science Research (DSR) and Human-Centered Design (HCD) [...] Read more.
This paper presents the conceptual design of the HumanEnerg Hotspot, an agile toolkit aimed at addressing the human energy crisis in the context of Industry 5.0. The toolkit has been developed using a blend of Design Science Research (DSR) and Human-Centered Design (HCD) methodologies, enabling a comprehensive human-centered problem identification and solution-seeking approach. The toolkit includes a variety of strategies, techniques, frameworks, and resource recommendations for industry use and has been designed to be easily adaptable for use in diverse industry settings. The toolkit is intended to support the European Union’s goal for industry to influence society through a human-centric approach to Industry 5.0 by prioritizing human energy reinforcement and creating a more resilient and productive workforce. The toolkit provides a valuable resource for employees and managers alike and offers a promising solution for addressing the human energy crisis in the era of Industry 5.0. Full article
(This article belongs to the Special Issue Advancement in Smart Manufacturing and Industry 4.0)
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21 pages, 469 KiB  
Article
Artificial Intelligence Software Adoption in Manufacturing Companies
by Klemen Kovič, Polona Tominc, Jasna Prester and Iztok Palčič
Appl. Sci. 2024, 14(16), 6959; https://doi.org/10.3390/app14166959 - 8 Aug 2024
Viewed by 1742
Abstract
This study investigates the adoption of artificial intelligence (AI) software in manufacturing companies in Slovenia, Slovakia and Croatia, and across six production areas. This research ad-dresses a gap in the literature regarding AI software implementation in relation to company size, technology intensity and [...] Read more.
This study investigates the adoption of artificial intelligence (AI) software in manufacturing companies in Slovenia, Slovakia and Croatia, and across six production areas. This research ad-dresses a gap in the literature regarding AI software implementation in relation to company size, technology intensity and supply chain role, and examines whether Industry 4.0 (I4.0) readiness influences AI adoption. Data from the European Manufacturing Survey 2022 were analyzed, and showed that the use of AI is still relatively low. On average only 18.4% of companies use AI software in at least one production area. Logistic regression analysis revealed that neither company size nor role in the supply chain or technology intensity are statistically significantly related to AI usage. However, a significant positive relationship was found between I4.0 readiness and AI adoption, suggesting that companies with advanced digital infrastructures and integrated cyber-physical systems are more likely to adopt AI. This finding underlines the importance of digital transformation for the integration of AI software. The study concludes that while company characteristics such as size and the role of the company in the supply chain are not statistically significantly related to the use of AI, the level of digital readiness is crucial. Full article
(This article belongs to the Special Issue Advancement in Smart Manufacturing and Industry 4.0)
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27 pages, 13026 KiB  
Article
Cybernetic Model Design for the Qualification of Pharmaceutical Facilities
by Ilija Tabasevic, Dragan D. Milanovic, Vesna Spasojevic Brkic, Mirjana Misita and Aleksandar Zunjic
Appl. Sci. 2024, 14(13), 5525; https://doi.org/10.3390/app14135525 - 25 Jun 2024
Viewed by 1019
Abstract
In this paper, an integrated cybernetic model for managing qualification activities when commissioning pharmaceutical facilities is created, focusing on defining critical factors that provide all the prerequisites for the start of the production process. An eight-year research and work on complex projects in [...] Read more.
In this paper, an integrated cybernetic model for managing qualification activities when commissioning pharmaceutical facilities is created, focusing on defining critical factors that provide all the prerequisites for the start of the production process. An eight-year research and work on complex projects in the pharmaceutical industry is integrated into a scientific research endeavor focused on the qualification of pharmaceutical facilities. The newly designed cybernetic model for the qualification of pharmaceutical facilities is flexible and adaptive and has the most adequate elements so far recognized in practice and enables the qualification of smart facilities, in accordance with the concept of Pharma 4.0. Additionally, it meets the requirements of the regulatory authorities; therefore, it constantly initiates the search for better solutions and process improvements. Moreover, it is universal and, thus, applicable to all reconstructions in the pharmaceutical industry. The application of the designed model has been implemented in practice and has shown outstanding results, as it combines diversity and sustainability in project management. Also, the model focuses on indicating aspects that include risk management, scientific approach, experimental testing, numerical simulations, as well as the possibility of optimization and energy saving. Full article
(This article belongs to the Special Issue Advancement in Smart Manufacturing and Industry 4.0)
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16 pages, 18966 KiB  
Article
Monitoring Equipment Malfunctions in Composite Material Machining: Acoustic Emission-Based Approach for Abrasive Waterjet Cutting
by Ioan Alexandru Popan, Cosmin Cosma, Alina Ioana Popan, Vlad I. Bocăneț and Nicolae Bâlc
Appl. Sci. 2024, 14(11), 4901; https://doi.org/10.3390/app14114901 - 5 Jun 2024
Cited by 3 | Viewed by 987
Abstract
This paper introduces an Acoustic Emission (AE)-based monitoring method designed for supervising the Abrasive Waterjet Cutting (AWJC) process, with a specific focus on the precision cutting of Carbon Fiber-Reinforced Polymer (CFRP). In industries dealing with complex CFRP components, like the aerospace, automotive, or [...] Read more.
This paper introduces an Acoustic Emission (AE)-based monitoring method designed for supervising the Abrasive Waterjet Cutting (AWJC) process, with a specific focus on the precision cutting of Carbon Fiber-Reinforced Polymer (CFRP). In industries dealing with complex CFRP components, like the aerospace, automotive, or medical sectors, preventing cutting system malfunctions is very important. This proposed monitoring method addresses issues such as reductions or interruptions in the abrasive flow rate, the clogging of the cutting head with abrasive particles, the wear of cutting system components, and drops in the water pressure. Mathematical regression models were developed to predict the root mean square of the AE signal. The signal characteristics are determined, considering key cutting parameters like the water pressure, abrasive mass flow rate, feed rate, and material thickness. Monitoring is conducted at both the cutting head and on the CFRP workpiece. The efficacy of the proposed monitoring method was validated through experimental tests, confirming its utility in maintaining precision and operational integrity in AWJC processes applied to CFRP materials. Integrating the proposed monitoring technique within the framework of digitalization and Industry 4.0/5.0 establishes the basis for advanced technologies such as Sensor Integration, Data Analytics and AI, Digital Twin Technology, Cloud and Edge Computing, MES and ERP Integration, and Human-Machine Interface. This integration enhances operational efficiency, quality control, and predictive maintenance in the AWJC process. Full article
(This article belongs to the Special Issue Advancement in Smart Manufacturing and Industry 4.0)
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16 pages, 2982 KiB  
Article
The Evaluation of Industry 5.0 Concepts: Social Network Analysis Approach
by Dragana Slavic, Ugljesa Marjanovic, Nenad Medic, Nenad Simeunovic and Slavko Rakic
Appl. Sci. 2024, 14(3), 1291; https://doi.org/10.3390/app14031291 - 4 Feb 2024
Cited by 11 | Viewed by 2306
Abstract
During 2022 and 2023, Industry 5.0 attracted a lot of attention. Many articles and papers regarding the basics of Industry 5.0, its pillars, and a comparison of Industry 5.0 and Industry 4.0, Society 5.0, and Operator 5.0 have been published. Although the concept [...] Read more.
During 2022 and 2023, Industry 5.0 attracted a lot of attention. Many articles and papers regarding the basics of Industry 5.0, its pillars, and a comparison of Industry 5.0 and Industry 4.0, Society 5.0, and Operator 5.0 have been published. Although the concept of Industry 5.0 is relatively new, companies from developed countries that have a high level of implementation of Industry 4.0 have already started the transition to Industry 5.0. Even though Industry 5.0 enables developing countries to become a part of developed countries’ value chains, it is not known which path to Industry 5.0 developing countries are taking. To fill this gap, the authors proposed research questions regarding the key indicators for measuring the levels of implementation of Industry 5.0 approaches in the manufacturing sector of the Republic of Serbia. This research includes insights from 146 manufacturing companies, gathered in 2022 as a part of the European Manufacturing Survey. The main findings of this study show that the most important indicator when it comes to human-centricity is training and competence development of production employees with a task-specific focus; the implementation of measures for improving efficiency in material consumption is significant for achieving sustainability; and the use of standardized and detailed work instructions is crucial in order to become resilient. Full article
(This article belongs to the Special Issue Advancement in Smart Manufacturing and Industry 4.0)
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Review

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29 pages, 593 KiB  
Review
Systematic Analysis of Risks in Industry 5.0 Architecture
by Muhammad Ali Hassan, Shehnila Zardari, Muhammad Umer Farooq, Marwah M. Alansari and Shimaa A. Nagro
Appl. Sci. 2024, 14(4), 1466; https://doi.org/10.3390/app14041466 - 11 Feb 2024
Cited by 6 | Viewed by 5107
Abstract
Industry 4.0, which was proposed ten years ago to address both the industry’s strengths and faults, has finally been replaced by Industry 5.0. It seeks to put human welfare at the core of manufacturing systems, achieving societal goals beyond employment and growth to [...] Read more.
Industry 4.0, which was proposed ten years ago to address both the industry’s strengths and faults, has finally been replaced by Industry 5.0. It seeks to put human welfare at the core of manufacturing systems, achieving societal goals beyond employment and growth to firmly provide wealth for the long-term advancement of all of humanity. The purpose of this research is to examine the risks involved in the adoption of Industry 5.0’s architecture. The paper discusses the significance of Industry 5.0 and the advanced technology needed for this industrial revolution, followed by a detailed discussion of Industry 5.0’s human-centric strategy. The comprehensive literature review has resulted in the identification of risks and their mitigation strategies in Industry 5.0 architecture. A taxonomy with respect to different categories of risks has also been proposed. This study classifies Industry 5.0 system assets, identifies platform-independent risks, and develops countermeasures to protect against potential threats, irrespective of the business or domain. Full article
(This article belongs to the Special Issue Advancement in Smart Manufacturing and Industry 4.0)
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