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Industry 5.0—the Human Factors in Semi-automated Manufacturing

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 4432

Special Issue Editors


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Guest Editor
Department of Process Engineering, University of Pannonia, H-8200 Veszprém, Hungary
Interests: process mining algorithms; discrete-event simulators; Industry 4.0; Operator 4.0; Industry 5.0
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
MTA-PE Lendület Complex Systems Monitoring Research Group, Department of Process Engineering, University of Pannonia, H-8200 Veszprém, Hungary
Interests: chemical engineering; complex systems; computational intelligence; network science; process engineering
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute of Industrial Automation and Software Engineering, University of Stuttgart, 70049 Stuttgart, Germany
Interests: networked automation systems; artificial intelligence; dependability

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Guest Editor
Institute of Industrial Automation and Software Engineering, University of Stuttgart, 70049 Stuttgart, Germany
Interests: intelligent automation; human digital twin; cyber-physical systems; reliability

Special Issue Information

Dear Colleagues,

Manufacturing companies face two major trends affecting their business: intelligent automatization and collaboration. After the first wave of digitization, new and modernized technologies such as integrated sensors, advanced robotics, and artificial intelligence led to the so-called Smart Manufacturing as part of Industry 4.0. Collaboration between the human operators and machines are completed thanks to the collaborative robots. Companies have realized that they still need humans at the shop floor besides high-level automation. This realization creates a new generation of the industrial revolution. Industry 5.0 brings human workers back to the factory floors. The Fifth Industrial Revolution will pair humans and machines to utilize human creativity to further increase process efficiency by combining workflows with intelligent systems. While the primary concern in Industry 4.0 is about automation, Industry 5.0 will be a synergy between humans and autonomous machines.

With the rapid development of innovative technologies, such as artificial intelligence methods, big data and cloud computing, the new concept of Industry 5.0 has been revolutionizing production and logistics systems by introducing collaborative processes and data-based operator support (so-called Operator 4.0). This Special Issue aims to disseminate advanced research in the theory and application of collaboration in the manufacturing industries (also known by some experts as Industry 5.0).

The main focus of this session is the invitation of high-quality, state-of-the-art research papers that deal with challenging issues in Industry 5.0. We solicit original papers of unpublished and completed research not currently under review by any other conference/magazine/journal.

Topics of interest include, but are not limited to:

  • Human–machine interface in IIoT for industrial applications;
  • Digital Twin, Device Models, Adaptive- and Automation-Models;
  • Human–machine interfaces (HMI) and SCADA supervisory systems;
  • Human factors, industrial ergonomics, and safety in smart maintenance;
  • Industrial applications of the Internet of Things;
  • AI- or ML-based maintenance;
  • Risk analysis for Industry Production Systems;
  • Smart Manufacturing;
  • Smart logistics related to industrial applications;
  • Cyberphysical systems;
  • Industrial sensor networks;
  • Combinations of sensors/sensor networks and Augmented Reality in industrial environments;
  • Real-time locating in production and logistics;
  • Process modelling and simulation.

Dr. Tamás Ruppert
Prof. Dr. János Abonyi
Prof. Dr. Andrey Morozov
Dr. Nasser Jazdi
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

  • human factors
  • intelligent automation
  • intelligent collaboration
  • industry 5.0
  • smart manufacturing
  • Human-Digital Twin

Published Papers (1 paper)

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Review

22 pages, 4868 KiB  
Review
Workforce Learning Curves for Human-Based Assembly Operations: A State-of-the-Art Review
by Carlos Peña, David Romero and Julieta Noguez
Appl. Sci. 2022, 12(19), 9608; https://doi.org/10.3390/app12199608 - 24 Sep 2022
Cited by 1 | Viewed by 3050
Abstract
In this state-of-the-art review, the authors explore the recent advancements in the topics of learning curve models and their estimation methods for manual operations and processes as well as the data collection and monitoring technologies used for supporting these. This objective is achieved [...] Read more.
In this state-of-the-art review, the authors explore the recent advancements in the topics of learning curve models and their estimation methods for manual operations and processes as well as the data collection and monitoring technologies used for supporting these. This objective is achieved by answering the following three research questions: (RQ1) What calculation methods for estimating the learning curve of a worker exist in the recent scientific literature? (RQ2) What other usages are manufacturing enterprises giving to the modern learning curve prediction models according to the recent scientific literature? and (RQ3) What data collection and monitoring technologies exist to automatically acquire the data needed to create and continuously update the learning curve of an assembly operator? To do so, the PRISMA methodology for literature reviews was used, only including journal articles and conference papers referencing the topic of manual operations and processes, and to fulfil the criteria of a state-of-the-art review, only the literary corpus generated in the last five years (from 2017 to 2022) was reviewed. The scientific databases where the explorative research was carried out were Scopus and Web of Science. Such research resulted in 11 relevant journal articles and international conference papers, which were first reviewed, synthesized, and then compared. Four estimating methods were found for learning curves, and one recently developed learning curve model was found. As for the data collection and monitoring technologies, six frameworks were found and reviewed. Lastly, in the discussion, different areas of opportunity were found in the current state-of-the-art, mainly by combining the existing learning curve models and their estimation methods and feeding these with modern real-time data collection and monitoring frameworks. Full article
(This article belongs to the Special Issue Industry 5.0—the Human Factors in Semi-automated Manufacturing)
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