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Systems
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Smart Manufacturing Systems for Industry 5.0
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Smart Manufacturing Systems for Industry 5.0

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Systems Practice in Engineering".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 14338

Special Issue Editors

Dr. Francesca Montagna

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Guest Editor
Politecnico di Torino, Turin, Italy
Interests: engineering design; technology management; innovation management
Prof. Dr. Daniel Brissaud

E-Mail Website
Guest Editor
Grenoble Institute of Technology, 38031 Grenoble, France
Interests: collaborative manufacturing systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ola Isaksson

E-Mail Website
Guest Editor
Division of Product Development, Department of Industrial and Materials Science, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
Interests: multidisciplinary design; value driven design; collaborative engineering; platform based development; design automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart manufacturing systems are integrated and collaborative manufacturing systems, equipped with advanced sensors in order to collect a massive quantity of data directly from operating environments (Zheng et al., 2018). Originally, the main purpose was to enable new manufacturing opportunities and to develop novel game rules to increase productivity and efficiency through collaboration, as well as to monitor production processes in real-time, develop measures to increase such productivity and efficiency (Jiafu et al., 2016; Thames and Schaefer, 2016), or to reduce production costs.

These goals actually made the collaborative aspects in production environment even more pressing and the role of humans increasingly central within working environments and beyond. Industry 5.0 considers the wellbeing of the worker at the centre of the factory and conceives new technologies as a tool to provide prosperity and growth for individuals and companies while respecting the production limits of the planet.

These objectives then involve challenges that not only look at the design of new manufacturing systems, the industrialization of new technologies, the interoperability and integration of such systems, and the management and the combination of large volumes of data, but they also shift from human–machine interactions to new challenges about the effects on humans introduced by such smart systems. The effects on the health and well-being of humans are not to be underestimated (Merhar et al., 2019; Reiman et al., 2021), nor are the effects on the planet (Olah et al., 2020; Stock & Seliger, 2016). Therefore, whether these smart systems can effectively enable sustainable growth is still to be comprehended.

The main purpose of this Special Issue is to delve into smart systems with applications in the manufacturing sphere and their consequences on individuals, working environments, and the related externalities:

  • Smart factory architecture and infrastructure and design of manufacturing systems;
  • Smart manufacturing processes and industrialization of new technologies;
  • Smart manufacturing system performance, methods of modelling, simulation;
  • ICT for Smart factories, interoperability, and data integration methods;
  • Human–systems interaction;
  • Human factors and ergonomics in Smart factories;
  • Any other topic related to the Special Issue's scope.

Dr. Francesca Montagna
Prof. Dr. Daniel Brissaud
Prof. Dr. Ola Isaksson
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. Systems 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.

Published Papers (7 papers)

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Research

Jump to: Review

19 pages, 2990 KiB  
Article
Design of a Digital Twin in Low-Volume, High-Mix Job Allocation and Scheduling for Achieving Mass Personalization
by Sheron K. H. Sit and Carman K. M. Lee
Systems 2023, 11(9), 454; https://doi.org/10.3390/systems11090454 - 01 Sep 2023
Cited by 1 | Viewed by 1173
Abstract
The growing consumer demand for unique products has made customization and personalization essential in manufacturing. This shift to low-volume, high-mix (LVHM) production challenges the traditional paradigms and creates difficulties for small and medium-sized enterprises (SMEs). Industry 5.0 emphasizes the importance of human workers [...] Read more.
The growing consumer demand for unique products has made customization and personalization essential in manufacturing. This shift to low-volume, high-mix (LVHM) production challenges the traditional paradigms and creates difficulties for small and medium-sized enterprises (SMEs). Industry 5.0 emphasizes the importance of human workers and social sustainability in adapting to these changes. This study introduces a digital twin design tailored for LVHM production, focusing on the collaboration between human expertise and advanced technologies. The digital twin-based production optimization system (DTPOS) uses an intelligent simulation-based optimization model (ISOM) to balance productivity and social sustainability by optimizing job allocation and scheduling. The digital twin model fosters a symbiotic relationship between human workers and the production process, promoting operational excellence and social sustainability through local innovation and economic growth. A case study was conducted within the context of a printed circuit board assembly (PCBA) using surface mount technology to validate the digital twin model’s efficacy and performance. The proposed DTPOS significantly improved the performance metrics of small orders, reducing the average order processing time from 19 days to 9.59 days—an improvement of 52.63%. The average order-to-delivery time for small orders was 19.47 days, indicating timely completion. These findings highlight the successful transformation from mass production to mass personalization, enabling efficient production capacity utilization and improved job allocation and scheduling. By embracing the principles of Industry 5.0, the proposed digital twin model addresses the challenges of LVHM production, fostering a sustainable balance between productivity, human expertise, and social responsibility. Full article
(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0)
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34 pages, 3195 KiB  
Article
An Experimental Protocol for Human Stress Investigation in Manufacturing Contexts: Its Application in the NO-STRESS Project
by Ainhoa Apraiz, Ganix Lasa, Francesca Montagna, Graziana Blandino, Erika Triviño-Tonato and Angel Dacal-Nieto
Systems 2023, 11(9), 448; https://doi.org/10.3390/systems11090448 - 31 Aug 2023
Cited by 1 | Viewed by 1484
Abstract
Stress is a critical concern in manufacturing environments, as it impacts the well-being and performance of workers. Accurate measurement of stress is essential for effective intervention and mitigation strategies. This paper introduces a holistic and human-centered protocol to measure stress in manufacturing settings. [...] Read more.
Stress is a critical concern in manufacturing environments, as it impacts the well-being and performance of workers. Accurate measurement of stress is essential for effective intervention and mitigation strategies. This paper introduces a holistic and human-centered protocol to measure stress in manufacturing settings. The three-phased protocol integrates the analysis of physiological signals, performance indicators, and the human perception of stress. The protocol incorporates advanced techniques, such as electroencephalography (EEG), heart rate variability (HRV), galvanic skin response (GSR), and electromyography (EMG), to capture physiological responses associated with stress. Furthermore, the protocol considers performance indicators as an additional dimension of stress measurement. Indicators such as task execution time, errors, production rate, and other relevant performance metrics contribute to a comprehensive understanding of stress in manufacturing environments. The human perception of stress is also integrated into the protocol, recognizing the subjective experience of the individual. This component captures self-assessment and subjective reports, allowing for a more nuanced evaluation of stress levels. By adopting a holistic and human-centered approach, the proposed protocol aims to enhance our understanding of stress factors in manufacturing environments. The protocol was also applied in the automotive industry and plastic component manufacturing. The insights gained from this protocol can inform targeted interventions to improve worker well-being, productivity, and overall organizational performance. Full article
(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0)
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20 pages, 2782 KiB  
Article
Virtual-Simulation-Based Multi-Objective Optimization of an Assembly Station in a Battery Production Factory
by Andreas Lind, Veeresh Elango, Lars Hanson, Dan Högberg, Dan Lämkull, Pär Mårtensson and Anna Syberfeldt
Systems 2023, 11(8), 395; https://doi.org/10.3390/systems11080395 - 02 Aug 2023
Cited by 1 | Viewed by 1252
Abstract
The planning and design process of manufacturing factory layouts is commonly performed using digital tools, enabling engineers to define and test proposals in virtual environments before implementing them physically. However, this approach often relies on the experience of the engineers involved and input [...] Read more.
The planning and design process of manufacturing factory layouts is commonly performed using digital tools, enabling engineers to define and test proposals in virtual environments before implementing them physically. However, this approach often relies on the experience of the engineers involved and input from various cross-disciplinary functions, leading to a time-consuming and subjective process with a high risk of human error. To address these challenges, new tools and methods are needed. The Industry 5.0 initiative aims to further automate and assist human tasks, reinforcing the human-centric perspective when making decisions that influence production environments and working conditions. This includes improving the layout planning process by making it more objective, efficient, and capable of considering multiple objectives simultaneously. This research presents a demonstrator solution for layout planning using digital support, incorporating a virtual multi-objective optimization approach to consider safety regulations, area boundaries, workers’ well-being, and walking distance. The demonstrator provides a cross-disciplinary and transparent approach to layout planning for an assembly station in the context of battery production. The demonstrator solution illustrates how layout planning can become a cross-disciplinary and transparent activity while being automated to a higher degree, providing results that support decision-making and balance cross-disciplinary requirements. Full article
(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0)
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24 pages, 2360 KiB  
Article
Decarbonization Measure: A Concept towards the Acceleration of the Automotive Plant Decarbonization
by Sami Alexandre Succar, Daniel Brissaud, Damien Evrard, Dominik Flick and Damien De la Fontaine
Systems 2023, 11(7), 335; https://doi.org/10.3390/systems11070335 - 01 Jul 2023
Viewed by 1250
Abstract
Smart manufacturing systems enable simultaneous addressing of productivity, sustainability, and social improvements. The implementation of such systems in industries such as the automotive industry represents a promising way to meet stakeholders’ requirements concerning the decarbonization of their productive activities. In fact, this task [...] Read more.
Smart manufacturing systems enable simultaneous addressing of productivity, sustainability, and social improvements. The implementation of such systems in industries such as the automotive industry represents a promising way to meet stakeholders’ requirements concerning the decarbonization of their productive activities. In fact, this task is truly challenging for the automotive industry considering their complex organizational issues, generating knowledge sharing problems, and the diversity of plants’ context and characteristics. These facts make any decarbonization solution local, instead of being spread to a maximum of production units to potentially enhance decarbonization time efficiency. This article tackles these issues by providing a new organizational concept dealing with the relationships between decarbonization actors (energy managers and consultants) supported by the technical design of an IT knowledge management tool. These contributions will be based on the concept of decarbonization measure (DM) and illustrated by the case of Stellantis, one of the world leaders in terms of vehicle production, which develops a new organizational structure from local energy managers to corporate energy consultants. Full article
(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0)
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26 pages, 8040 KiB  
Article
A Framework for Service-Oriented Digital Twin Systems for Discrete Workshops and Its Practical Case Study
by Qinglei Zhang, Yang Wei, Zhen Liu, Jianguo Duan and Jiyun Qin
Systems 2023, 11(3), 156; https://doi.org/10.3390/systems11030156 - 19 Mar 2023
Cited by 2 | Viewed by 1902
Abstract
To address issues in discrete manufacturing workshops, such as the difficulty for management personnel to coordinate workshop production and the challenge of visualizing and supervising a massive amount of temporary data, this paper proposes a service-oriented digital-twin-system framework for discrete workshops using the [...] Read more.
To address issues in discrete manufacturing workshops, such as the difficulty for management personnel to coordinate workshop production and the challenge of visualizing and supervising a massive amount of temporary data, this paper proposes a service-oriented digital-twin-system framework for discrete workshops using the industrial IoT platform as the system-service platform to solve the problems of the opaque monitoring of operators in discrete workshops, the low interactivity of 2D monitoring systems, and the difficulty of the visual monitoring of workshop data. Firstly, the current situation of intelligent manufacturing workshop-monitoring demand in the context of new-generation information technology is analyzed, and a six-dimensional digital-twin-workshop-monitoring architecture is proposed, whereby a discrete workshop monitoring system based on the digital twin is constructed with IoT as the service platform. We will conduct research on the construction of virtual workshops for the system development process, twin data collection based on edge computing gateways, and dynamic monitoring of the production process. Finally, through the application of this system framework in a movable-arm-production workshop, the more intelligent human–machine interaction process of browsing and controlling workshop information, such as the equipment layout and production processes in the virtual workshop, has been realized. This includes data acquisition based on edge-computing gateways, dynamic real-time monitoring of the production process, etc., which provides a reference for realizing the visual monitoring of the discrete workshop. Full article
(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0)
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Review

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26 pages, 3733 KiB  
Review
How to Measure Stress in Smart and Intelligent Manufacturing Systems: A Systematic Review
by Graziana Blandino
Systems 2023, 11(4), 167; https://doi.org/10.3390/systems11040167 - 23 Mar 2023
Cited by 2 | Viewed by 1858
Abstract
The Fourth Industrial Revolution has introduced innovative technologies to manufacturing, resulting in digital production systems with consequences on workers’ roles and well-being. From the literature emerges the necessity to delve into the work-related stress phenomenon since it affects workers’ health status and performance [...] Read more.
The Fourth Industrial Revolution has introduced innovative technologies to manufacturing, resulting in digital production systems with consequences on workers’ roles and well-being. From the literature emerges the necessity to delve into the work-related stress phenomenon since it affects workers’ health status and performance and companies’ productivity. This review summarises the stress indicators and other influential factors in order to contribute to a stress assessment of human workers in smart and intelligent manufacturing systems. The PRISMA methodology is adopted to select studies consistent with the aim of the study. The analysis reviews objective measurements, such as physical, physiological, and subjective measurements, usually driven by a psychological perspective. In addition, experimental protocols and environmental and demographic variables that influence stress are illustrated. However, the investigation of stress indicators combined with other factors leads to more reliable and effective results. Finally, it is discovered that standards regarding stress indicators and research variables investigated by experimental studies are lacking. In addition, it is revealed that environmental and demographic variables, which may reveal significant suggestions for stress investigation, are rather neglected. This review provides a theorical summary of stress indicators for advanced manufacturing systems and highlights gaps to inspire future studies. Moreover, it provides practical guidelines to analyse other factors that may influence stress evaluation. Full article
(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0)
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25 pages, 1329 KiB  
Review
Human-in-Loop: A Review of Smart Manufacturing Deployments
by Mangolika Bhattacharya, Mihai Penica, Eoin O’Connell, Mark Southern and Martin Hayes
Systems 2023, 11(1), 35; https://doi.org/10.3390/systems11010035 - 06 Jan 2023
Cited by 10 | Viewed by 3516
Abstract
The recent increase in computational capability has led to an unprecedented increase in the range of new applications where machine learning can be used in real time. Notwithstanding the range of use cases where automation is now feasible, humans are likely to retain [...] Read more.
The recent increase in computational capability has led to an unprecedented increase in the range of new applications where machine learning can be used in real time. Notwithstanding the range of use cases where automation is now feasible, humans are likely to retain a critical role in the operation and certification of manufacturing systems for the foreseeable future. This paper presents a use case review of how human operators affect the performance of cyber–physical systems within a ’smart’ or ’cognitive’ setting. Such applications are classified using Industry 4.0 (I4.0) or 5.0 (I5.0) terminology. The authors argue that, as there is often no general agreement as to when a specific use case moves from being an I4.0 to an I5.0 example, the use of a hybrid Industry X.0 notation at the intersection between I4.0 and I5.0 is warranted. Through a structured review of the literature, the focus is on how secure human-mediated autonomous production can be performed most effectively to augment and optimise machine operation. Full article
(This article belongs to the Special Issue Smart Manufacturing Systems for Industry 5.0)
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