Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
3.0
Journals
Machines
Special Issues
Human-Centered Manufacturing in the Era of Industry 5.0: Toward Personalized...
share announcement

Human-Centered Manufacturing in the Era of Industry 5.0: Toward Personalized and Intelligent Collaborative Production Systems

A Special Issue of Machines (ISSN 2075-1702) belonging to the section "Industrial Systems".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 3017

Editors

Dr. Robert Ojstersek

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia
Interests: production planning; sustainable manufacturing; intelligent manufacturing and evaluating collaborative workplace on manufacturing systems efficiency
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Iztok Palčič

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, University of Maribor, 2000 Maribor, Slovenia
Interests: Industry 4.0; Industry 5.0; Industry 4.0/5.0 readiness and maturity models; production system design; collaborative workplace
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The transition from Industry 4.0 to Industry 5.0 marks a paradigm shift from automation-centric to human-centric manufacturing. This Special Issue focuses on emerging trends, technologies, and methods that enable personalized, resilient, and sustainable production systems through the integration of human skills and intelligent machines. Topics of interest include the design and implementation of collaborative workspaces, adaptive and personalized manufacturing, human–machine interaction, and socio-technical system optimization. We welcome contributions that explore theoretical foundations, novel frameworks, industrial applications, and case studies related to human-centered design, digital twins, AI-driven customization, and the social dimensions of smart manufacturing. Emphasis will be placed on multidisciplinary approaches that bridge engineering, human factors, and information technology to create production systems that are not only efficient but also inclusive, flexible, and aligned with the values of Industry 5.0. This Special Issue aims to provide a platform on which researchers and practitioners may share insights and innovations that will shape the future of intelligent, collaborative, and personalized manufacturing ecosystems.

Dr. Robert Ojstersek
Prof. Dr. Iztok Palčič
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • Industry 5.0
  • human-centric manufacturing
  • collaborative workspaces
  • human–machine interaction
  • human–robot collaboration
  • socio-technical systems
  • digital twins
  • smart manufacturing
  • resilient production systems
  • sustainable manufacturing
  • cyber–physical systems
  • AI in manufacturing
  • future manufacturing ecosystems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

25 pages, 1458 KB  
Article
A Digital Twin Framework for Multimodal Operator-Centered Human–Cobot Collaboration in Assembly Tasks
by David Alfaro-Viquez, Mauricio Zamora-Hernandez, Michael Fernandez-Vega, David Ortiz-Perez, Jose Garcia-Rodriguez and Jorge Azorin-Lopez
Machines 2026, 14(7), 780; https://doi.org/10.3390/machines14070780 - 12 Jul 2026
Viewed by 428
Abstract
Current digital twin frameworks focused on human–robot collaboration rarely take into account the sensory degradation of real industrial environments, nor do they integrate the operator as an active agent within the system. This research presents a multimodal digital twin framework for a dual-arm [...] Read more.
Current digital twin frameworks focused on human–robot collaboration rarely take into account the sensory degradation of real industrial environments, nor do they integrate the operator as an active agent within the system. This research presents a multimodal digital twin framework for a dual-arm collaborative robot at an assembly station; the system was developed using ROS 2 Jazzy and CoppeliaSim as the simulator. The architecture integrates three main components: the first is a perception layer that captures voice commands using Whisper ASR and the state of the workspace using a hybrid YOLO + ViT visual pipeline, both with per-channel metadata; the second consists of a Confidence-Weighted Late Fusion engine that dynamically adjusts the weight of each modality based on real-time signal quality, so that each fusion decision can be reconstructed from the signals that generated it; and the third component is a Reference Resolver that grounds linguistic intent within the visual context of the scene and in the fusion weights, using a local instance of Llama 3.1 8B that does not transmit audio, transcripts, or images outside the system. The framework was evaluated using 210 iterations distributed across seven degradation conditions of increasing severity, comparing adaptive fusion against a baseline of fixed weights (0.5/0.5). Under clean conditions and under visual degradation of any severity, both configurations achieved 100% accuracy. Under severe auditory degradation (SNR 0 dB), adaptive fusion activated the safety gate and refrained from executing most commands (13.3% accuracy), while the fixed-weight baseline executed more commands (60% accuracy) but made three incorrect object selections; under severe dual degradation, the pattern repeated (13.3% vs. 40%, with five incorrect selections in the baseline). The adaptive system made no grounding errors in the 210 executions, compared to eight in the baseline, substituting incorrect execution with conservative abstention when no modality provided a reliable signal. The implementation, featuring a versioned degradation protocol and a fixed seed, provides a reproducible benchmark for evaluating multimodal fusion strategies in human–cobot interaction. Full article
(This article belongs to the Special Issue Human-Centered Manufacturing in the Era of Industry 5.0: Toward Personalized and Intelligent Collaborative Production Systems)
►▼ Show Figures

Figure 1

23 pages, 3103 KB  
Article
Designing Human–Robot Collaborative Workstations: An ECS-Based Framework for Efficiency and Worker Empowerment
by Malek Chakroun, Valérie Rocchi and Daniel Brissaud
Machines 2026, 14(6), 586; https://doi.org/10.3390/machines14060586 - 25 May 2026
Viewed by 490
Abstract
Human–robot collaborative workstations are increasingly deployed in industry, yet their design mainly focuses on productivity and ergonomics, with limited consideration of worker empowerment. This paper proposes a framework integrating Enabling Collaborative Situations (ECSs) into the design and evaluation of collaborative assembly systems. Following [...] Read more.
Human–robot collaborative workstations are increasingly deployed in industry, yet their design mainly focuses on productivity and ergonomics, with limited consideration of worker empowerment. This paper proposes a framework integrating Enabling Collaborative Situations (ECSs) into the design and evaluation of collaborative assembly systems. Following an exploratory design-based case study approach, the framework is implemented through the development of a physical demonstrator workstation in a realistic assembly context. It structures task allocation and interaction design while introducing an operational ECS evaluation grid combining industrial performance, ergonomics, and worker experience. The results suggest that collaboration design can preserve expected industrial performance while expanding worker autonomy, supporting work activity, and providing a basis for more empowering human–robot collaborative workstations. Full article
(This article belongs to the Special Issue Human-Centered Manufacturing in the Era of Industry 5.0: Toward Personalized and Intelligent Collaborative Production Systems)
►▼ Show Figures

Figure 1

15 pages, 1792 KB  
Article
Developing a Digital Twin for Human Performance Assessment in Human–Machine Interaction
by Erik Novak, Aljaž Javernik, Iztok Palčič and Robert Ojsteršek
Machines 2026, 14(3), 346; https://doi.org/10.3390/machines14030346 - 19 Mar 2026
Cited by 1 | Viewed by 1392
Abstract
Digital twins are becoming essential tools in smart, human-centric manufacturing, yet validated approaches that integrate real human behavior into digital twin models remain limited. This study develops and experimentally validates a digital twin as a tool for evaluating human performance in balancing human–machine [...] Read more.
Digital twins are becoming essential tools in smart, human-centric manufacturing, yet validated approaches that integrate real human behavior into digital twin models remain limited. This study develops and experimentally validates a digital twin as a tool for evaluating human performance in balancing human–machine interaction. A physical system comprising a conveyor belt, sensors, and operator-controlled elements was constructed, and a functionally equivalent digital model was created using Arduino IDE and MATLAB/Simulink. The digital twin records and synchronizes key human–machine interaction variables, including response time, assembly time, and execution consistency. Validation was conducted through simulation testing and an experimental study with 18 participants performing repeated assembly cycles. The results show that the developed digital twin accurately replicates the temporal dynamics of the physical process and reliably captures individual human performance patterns. Overall, the study provides a validated methodological framework for human–machine-integrated digital twins and demonstrates their potential for analyzing human–machine interaction, supporting operator training, and adaptive workplace design in line with Industry 5.0 principles. Full article
(This article belongs to the Special Issue Human-Centered Manufacturing in the Era of Industry 5.0: Toward Personalized and Intelligent Collaborative Production Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop