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Applied Sciences
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Intelligent, Sustainable and Resilient Personalized Product-Service Systems towards Industry 5.0
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Intelligent, Sustainable and Resilient Personalized Product-Service Systems towards Industry 5.0

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

Deadline for manuscript submissions: closed (20 May 2024) | Viewed by 6057

Special Issue Editors

Prof. Dr. Dimitris Mourtzis

E-Mail Website
Guest Editor
Department of Mechanical Engineering and Aeronautics, University of Patras, Rio Patras 26504, Greece
Interests: robotic systems; automation; augmented, mixed, and virtual reality in manufacturing; manufacturing process modeling; cloud technologies; Internet of Things (IoT); digital twin; 5G; artificial intelligence; product–service systems (PSS); Industry 4.0; Industry 5.0
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Carmen Constantinescu

E-Mail Website
Guest Editor
Fraunhofer Institute for Industrial Engineering—FhG IAO, Stuttgart, Germany
Interests: digital/virtual/smart factory; Manufacturing 4.0; modeling; simulation; wearable robotics; exoskeletons
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nikolaos Papakostas

E-Mail Website
Guest Editor
Laboratory for Advanced Manufacturing Simulation and Robotics (LAMS), School of Mechanical and Materials Engineering, University College Dublin, Dublin D04 V1W8, Ireland
Interests: digital manufacturing; manufacturing simulation; robotics; assembly processes; production planning and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The mass production paradigm, which has characterized manufacturing in recent decades, aims to achieve sustainable and competitive advantages. The increased and highly volatile consumer/customer demand for user-centered customization and the personalization of products and services present new challenges in manufacturing. The innovative mass personalization paradigm, not fully investigated under Industry 4.0, represents a high-tech manufacturing automation strategy, enabling the better integration and smoother cooperation of hardware devices and machinery (Internet of Things—IoT), software systems and humans in the extended manufacturing value chain. Smart factories, core of this new paradigm, are characterized by a high degree of digitalization and data-centricity. Jointly cooperative working teams of humans and robots autonomously guided by lights collaboratively operate in so-called Lights-out Factories. Extreme automation, developing until "everything is connected to everything else" creates vulnerabilities that have not yet been investigated extensively. The next industrial revolution, Industry 5.0, is expected to cope with these new challenges by bringing the efficiency of manufacturing systems and supply chains beyond limits. Industry 5.0 aims to democratize the co-production of knowledge by employing cutting-edge digital technologies, e.g., Big Data and IoT, supported by three main pillars: (a) human-centricity; (b) resilience; and (c) sustainability. One of the main focuses relies on the development of product platforms aiming to improve the production efficiency and create high added value for the customer/end-user.

This Special Issue explores cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), computer vision, edge computing, advanced sensors, collaborative, mobile and wearable robotics, real-time control and optimization and cognitive systems as promising technologies for the realization of mass personalization/customization under resilient, human-centric manufacturing.

Prof. Dr. Dimitris Mourtzis
Prof. Dr. Carmen Constantinescu
Prof. Dr. Nikolaos Papakostas
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.

Published Papers (3 papers)

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Research

35 pages, 7508 KiB  
Article
A Voice-Enabled ROS2 Framework for Human–Robot Collaborative Inspection
by Apostolis Papavasileiou, Stelios Nikoladakis, Fotios Panagiotis Basamakis, Sotiris Aivaliotis, George Michalos and Sotiris Makris
Appl. Sci. 2024, 14(10), 4138; https://doi.org/10.3390/app14104138 - 13 May 2024
Viewed by 548
Abstract
Quality inspection plays a vital role in current manufacturing practice since the need for reliable and customized products is high on the agenda of most industries. Under this scope, solutions enhancing human–robot collaboration such as voice-based interaction are at the forefront of efforts [...] Read more.
Quality inspection plays a vital role in current manufacturing practice since the need for reliable and customized products is high on the agenda of most industries. Under this scope, solutions enhancing human–robot collaboration such as voice-based interaction are at the forefront of efforts by modern industries towards embracing the latest digitalization trends. Current inspection activities are often based on the manual expertise of operators, which has been proven to be time-consuming. This paper presents a voice-enabled ROS2 framework towards enhancing the collaboration of robots and operators under quality inspection activities. A robust ROS2-based architecture is adopted towards supporting the orchestration of the process execution flow. Furthermore, a speech recognition application and a quality inspection solution are deployed and integrated to the overall system, showcasing its effectiveness under a case study deriving from the automotive industry. The benefits of this voice-enabled ROS2 framework are discussed and proposed as an alternative way of inspecting parts under human–robot collaborative environments. To measure the added value of the framework, a multi-round testing process took place with different parameters for the framework’s modules, showcasing reduced cycle time for quality inspection processes, robust HRI using voice-based techniques and accurate inspection. Full article
(This article belongs to the Special Issue Intelligent, Sustainable and Resilient Personalized Product-Service Systems towards Industry 5.0)
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18 pages, 3807 KiB  
Article
Designing a User-Centered Inspection Device’s Handle for the Aircraft Manufacturing Industry
by Ana Colim, Débora Pereira, Pedro Lima, André Cardoso, Rui Almeida, Duarte Fernandes, Sacha Mould and Pedro Arezes
Appl. Sci. 2023, 13(20), 11584; https://doi.org/10.3390/app132011584 - 23 Oct 2023
Cited by 1 | Viewed by 761
Abstract
In aircraft manufacturing settings, workers are frequently exposed to biomechanical risk factors, mainly in the later stages of the production processes, including inspection tasks. To support the development of a novel inspection device appropriate for the end-users and their tasks, this study presents [...] Read more.
In aircraft manufacturing settings, workers are frequently exposed to biomechanical risk factors, mainly in the later stages of the production processes, including inspection tasks. To support the development of a novel inspection device appropriate for the end-users and their tasks, this study presents a user-centered approach for the device’s handle. Three different handles were proposed, and the current study aims to find out which handle can offer (1) the best ergonomic conditions and (2) the best stability in holding the device in hand during an inspection task. To this end, 23 volunteers participated in the experimental assessment, which comprised qualitative and quantitative data. A questionnaire was used for subjective comfort assessment. Partial times to execute the task studied, stability metrics of the device during its handling, and kinematic and electromyographic data of the upper limb recruited were measured and analyzed to compare the three handles. Outstanding results include the higher comfort perceived by the participants working with the selected handle for the final design, as well as the reduction in muscle effort. Globally, the results obtained demonstrated that the handle user-centered design potentiates good efficiency and usability of the novel device. Full article
(This article belongs to the Special Issue Intelligent, Sustainable and Resilient Personalized Product-Service Systems towards Industry 5.0)
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21 pages, 3916 KiB  
Article
An Intelligent Product Service System for Adaptive Maintenance of Engineered-to-Order Manufacturing Equipment Assisted by Augmented Reality
by John Angelopoulos and Dimitris Mourtzis
Appl. Sci. 2022, 12(11), 5349; https://doi.org/10.3390/app12115349 - 25 May 2022
Cited by 18 | Viewed by 3049
Abstract
Under the framework of Industry 4.0, machines and machine tools have evolved to smart and connected things, comprising the Internet of Things (IoT) and leading to the Mass Personalization (MP) paradigm, which enables the production of uniquely made products at scale. MP, fueled [...] Read more.
Under the framework of Industry 4.0, machines and machine tools have evolved to smart and connected things, comprising the Internet of Things (IoT) and leading to the Mass Personalization (MP) paradigm, which enables the production of uniquely made products at scale. MP, fueled by individualization trends and enabled by increasing digitalization, has the potential to go beyond current mass customization. Although this evolution has enabled engineers to gain useful insight for the production, the machine status, the quality of products, etc., machines have become more complex. Thus, Maintenance Repair and Overhaul (MRO) operations should be undertaken by specialized personnel. Additionally, Augmented Reality (AR) can support remote maintenance assistance to handle unexpected malfunctions. Moreover, due to advances regarding Product Service Systems (PSS), manufacturing companies are offering many services to improve user experience. Consequently, in this manuscript the design and development of a method based on the principles of servitization for the provision of an intelligent and adaptable maintenance service assisted by AR are presented. The contribution of the manuscript extends to the provision of an optimization algorithm for adapting the schedules of the stakeholders based on the energy supplier predictions. The developed method was tested and validated on an industrial case study of injection mold maintenance, achieving 11% energy reduction, 50% less time for mold inspection, and a 20% rise in on-time mold deliveries. Full article
(This article belongs to the Special Issue Intelligent, Sustainable and Resilient Personalized Product-Service Systems towards Industry 5.0)
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