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Applied Sciences
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Industry 5.0.: Current Status, Challenges, and New Strategies
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Industry 5.0.: Current Status, Challenges, and New Strategies

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

Deadline for manuscript submissions: closed (1 November 2022) | Viewed by 9604

Special Issue Editors

Prof. Dr. Dragan Perakovic

E-Mail Website
Guest Editor
Department of Information and Communication Traffic, Faculty of Transport and Traffic Science, University of Zagreb, Vukelićeva 4, 10 000 Zagreb, Croatia
Interests: innovative communication; ecosystem; digital forensic communication; security; industry 4.0; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Lucia Knapčíková

E-Mail Website
Guest Editor
Department of Industrial Engineering and Informatics, Faculty of Manufacturing Technologies, Technical University of Košice, Bayerova 1, 08001 Prešov, Slovakia
Interests: industrial engineering; mechanical engineering; Industry 4.0; Industry 5.0; ICT; composites; waste materials; manufacturing; circular economy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industry 4.0 introduced automation technologies, smart manufacturing and IoT; Industry 5.0, however, offers the possibility of collaboration between machines and humans.

Although Industry 4.0 aimed to give priority to automation processes, we now see the opposite trend. Industry 5.0 aims to show us how humans and machines could collaborate. Machine–human interactions offer benefits from a range of different aspects.

However, the impact of each industrial revolution was to generate more effective processes, and Industry 5.0 will offer even better business models.

This Special Issue aims to explore part of the human­­–computer interaction, digital manufacturing, machine-human interaction and the latest automation technologies.

Prof. Dr. Dragan Perakovic
Dr. Lucia Knapčíková
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 5.0
  • digital manufacturing
  • production planning and control
  • smart manufacturing
  • automation systems
  • machine-human interaction

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Published Papers (3 papers)

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Research

12 pages, 4519 KiB  
Article
The Study of Machine Learning Assisted the Design of Selected Composites Properties
by Stella Hrehova and Lucia Knapcikova
Appl. Sci. 2022, 12(21), 10863; https://doi.org/10.3390/app122110863 - 26 Oct 2022
Cited by 9 | Viewed by 1817
Abstract
One of the basic points of Industry 5.0 is to make the industry sustainable. There is a need to develop circular processes that reuse, repurpose, and recycle natural resources, and thus, reduce waste. This part can also include composite materials, which were used [...] Read more.
One of the basic points of Industry 5.0 is to make the industry sustainable. There is a need to develop circular processes that reuse, repurpose, and recycle natural resources, and thus, reduce waste. This part can also include composite materials, which were used for some time in many areas. An essential feature of their applicability is the properties of these materials. The ratio of the individual components determines the properties of composite materials, and artificial intelligence machine learning (ML) techniques are already used to determine the optimal ratio. ML can be briefly described as computer science that uses existing data to predict future data. This approach is made possible by the current possibilities of collecting and analysing a large amount of data. It improves the chance of finding more variable influences (predictors) in the processes. These factors can be quantified more objectively; their mutual interactions can be identified, and, thanks to longer-term sampling, their future development behavior can be predictively modelled. The present article deals with the possibility of applying machine learning in predicting the absorption properties of composite material, which consists of a thermoplastic and matrix recycled polyvinyl butyral (PVB), obtained after recycling car glass windshields. Full article
(This article belongs to the Special Issue Industry 5.0.: Current Status, Challenges, and New Strategies)
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24 pages, 6579 KiB  
Article
Towards Flexible and Cognitive Production—Addressing the Production Challenges
by Muaaz Abdul Hadi, Daniel Kraus, Amer Kajmakovic, Josef Suschnigg, Ouijdane Guiza, Milot Gashi, Georgios Sopidis, Matej Vukovic, Katarina Milenkovic, Michael Haslgruebler, Markus Brillinger and Konrad Diwold
Appl. Sci. 2022, 12(17), 8696; https://doi.org/10.3390/app12178696 - 30 Aug 2022
Cited by 6 | Viewed by 3139
Abstract
Globalization in the field of industry is fostering the need for cognitive production systems. To implement modern concepts that enable tools and systems for such a cognitive production system, several challenges on the shop floor level must first be resolved. This paper discusses [...] Read more.
Globalization in the field of industry is fostering the need for cognitive production systems. To implement modern concepts that enable tools and systems for such a cognitive production system, several challenges on the shop floor level must first be resolved. This paper discusses the implementation of selected cognitive technologies on a real industrial case-study of a construction machine manufacturer. The partner company works on the concept of mass customization but utilizes manual labour for the high-variety assembly stations or lines. Sensing and guidance devices are used to provide information to the worker and also retrieve and monitor the working, with respecting data privacy policies. Next, a specified process of data contextualization, visual analytics, and causal discovery is used to extract useful information from the retrieved data via sensors. Communications and safety systems are explained further to complete the loop of implementation of cognitive entities on a manual assembly line. This deepened involvement of cognitive technologies are human-centered, rather than automated systems. The explained cognitive technologies enhance human interaction with the processes and ease the production methods. These concepts form a quintessential vision for an effective assembly line. This paper revolutionizes the existing industry 4.0 with an even-intensified human–machine interaction and moving towards cognitivity. Full article
(This article belongs to the Special Issue Industry 5.0.: Current Status, Challenges, and New Strategies)
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12 pages, 2721 KiB  
Article
Recent Application of Dijkstra’s Algorithm in the Process of Production Planning
by Marcel Behún, Dušan Knežo, Michal Cehlár, Lucia Knapčíková and Annamária Behúnová
Appl. Sci. 2022, 12(14), 7088; https://doi.org/10.3390/app12147088 - 14 Jul 2022
Cited by 12 | Viewed by 2744
Abstract
This paper aims to develop a method that could serve as a tool for evaluating extracted raw materials in terms of use by considering the place of extraction and consumption. Dijkstra´s algorithm solves many of the shortest path problems observed in the production [...] Read more.
This paper aims to develop a method that could serve as a tool for evaluating extracted raw materials in terms of use by considering the place of extraction and consumption. Dijkstra´s algorithm solves many of the shortest path problems observed in the production planning of raw materials. The algorithm requires knowledge of the relative distance between the vertices and the definition of the Euclidean distance of the vertices from the target vertex. The algorithm scans all of the paths and chooses the one with the minimum distance. At the same time, it would be able to identify the places of sale of raw materials and transport sites for the transportation of raw materials. It would have a database of point and line sources of occurrence (mining, deposit), places of transport (transmission network), and points of sale (seller). At present, geo-statistics is becoming an essential tool for solving various problems in modern deposit geology. Its results are used to calculate reserves and the economic valuation of the deposit. In the process of production planning, it is necessary to constantly process and analyze the geological information obtained during the mining survey. Full article
(This article belongs to the Special Issue Industry 5.0.: Current Status, Challenges, and New Strategies)
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