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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 May 2023) | Viewed by 53585

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Dr. Antonella Petrillo

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Department of Engineering, University of Naples “Parthenope”, 80143 Napoli, Italy
Interests: green transition; digitalization; operations management; industrial plant
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Dear Colleagues,

The transformation toward Smart Industrial Systems for all must be harmonized with the threats, opportunities, and dynamics of the industry revolution. In this scenario, it is also necessary to keep in mind, as many scientists and academics claim, that is true that digitization and innovation are two processes that guide today’s companies. However, we obviously cannot ignore that the COVID-19 pandemic represents a strong “technological accelerator”.

Identifying and outlining the technological, cultural, organizational, social, and managerial changes underlying the smart transformation underway is the aim of this Special Issue. The theme of the issue is therefore broad and affects not only the world of work in the strict sense, but also in a global vision, and it is a theme that also represents a challenge for the academic, professional, and industrial world. We are particularly interested in publishing articles not only from a traditional point of view but also from new emerging trends in order to meet practitioners’ needs and make theoretical contributions. All manuscripts are welcome in which the authors address smart industrial systems through the analysis of enabling technologies (e.g. artificial intelligence, quantum computing, robotics, etc.) as well as the impacts of new forms of work organization, from the need to reform training courses to the dangers of new forms of authoritarianism and social inequality.

The purpose of this Special Issue is to collect high-quality contemporary research articles on the topic of “Smart Industrial System” to enhance talent in the technology/innovation sector.

Prof. Dr. Antonella Petrillo
Guest Editor

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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

smart industrial systems and smart manufacturing
digital gender gap and gendered innovation
smart working
digital job skills (hard skills, soft skills, stem—science, technology, engineering, and math)
sustainable manufacturing, circular economy, bioeconomy (product life-cycle management, life cycle assessment, design for environment, etc.)
digitalization and 5G
blockchain and smart industry (applications, benefits, etc.)
enabling technology (IoT, quantum computing, simulation and digital twin, additive manufacturing, big data, cyber-physical systems, augmented reality, horizontal and vertical system integration, autonomous robot, virtual reality, machine learning, etc.)
decision analysis and decision support systems applications
smart manufacturing, production and predictive maintenance
smart factory in the metaverse

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Smart Industrial System, 2nd Edition in Applied Sciences

Published Papers (13 papers)

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Research

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15 pages, 8881 KiB

Open AccessArticle

RoboTwin Metaverse Platform for Robotic Random Bin Picking

by Cheng-Han Tsai, Eduin E. Hernandez, Xiu-Wen You, Hsin-Yi Lin and Jen-Yuan Chang

Appl. Sci. 2023, 13(15), 8779; https://doi.org/10.3390/app13158779 - 29 Jul 2023

Cited by 1 | Viewed by 1414

Abstract

Although vision-guided robotic picking systems are commonly used in factory environments, achieving rapid changeover for diverse workpiece types can still be challenging because the manual redefinition of vision software and tedious collection and annotation of datasets consistently hinder the automation process. In this paper, we present a novel approach for rapid workpiece changeover in a vision-guided robotic picking system using the proposed RoboTwin and FOVision systems. The RoboTwin system offers a realistic metaverse scene that enables tuning robot movements and gripper reactions. Additionally, it automatically generates annotated virtual images for each workpiece’s pickable point. These images serve as training datasets for an AI model and are deployed to the FOVision system, a platform that includes vision and edge computing capabilities for the robotic manipulator. The system achieves an instance segmentation mean average precision of 70% and a picking success rate of over 80% in real-world detection scenarios. The proposed approach can accelerate dataset generation by 80 times compared with manual annotation, which helps to reduce simulation-to-real gap errors and enables rapid line changeover within flexible manufacturing systems in factories. Full article

(This article belongs to the Special Issue Smart Industrial System)

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17 pages, 3537 KiB

Open AccessArticle

A Robust Statistical Methodology for Measuring Enterprise Agility

by Roberto Moraga-Díaz, Andrés Leiva-Araos and José García

Appl. Sci. 2023, 13(14), 8445; https://doi.org/10.3390/app13148445 - 21 Jul 2023

Cited by 4 | Viewed by 1992

Abstract

In an era characterized by rapid technological advancements, economic fluctuations, and global competition, adaptability and resilience have become critical success factors for businesses navigating uncertainty and complexity. This article explores the role of enterprise agility in today’s business landscape at Latam branch of Tata Consultancy Services, where organizations face complex and diverse operations. We aim to examine how companies can become more agile in the face of emerging challenges and seize opportunities swiftly to drive growth and deliver value. Since 2014, the division has embarked on an agile transformation journey to drive growth, deliver value, foster innovation, and build resilience in an increasingly dynamic environment. We scrutinize an approach to measuring and enhancing enterprise agility, employing statistical analysis and continuous improvement methodologies to tackle real-world challenges while offering valuable insights and recommendations for organizations aiming to implement similar systems. The results of an agile transformation in a certain company’s Latam branch serve as a compelling case study, demonstrating how the implementation of targeted measures and continuous improvement can significantly bolster enterprise agility. Methodologically, our work applies a novel sequence of parametric statistical tests which, to the best of our knowledge, have not been used in the industry to validate the results of business agility metrics. In future work, we aim to create a new workflow considering non-parametric tests to address data with other statistical distributions. We conclude our work by proposing a sequence of steps for organizations to implement business agility metrics. Full article

(This article belongs to the Special Issue Smart Industrial System)

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23 pages, 3715 KiB

Open AccessArticle

A Basic Description Logic for Service-Oriented Architecture in Factory Planning and Operational Control in the Age of Industry 4.0

by Angela Luft, Nils Luft and Kristian Arntz

Appl. Sci. 2023, 13(13), 7610; https://doi.org/10.3390/app13137610 - 27 Jun 2023

Cited by 3 | Viewed by 1618

Abstract

Manufacturing companies across multiple industries face an increasingly dynamic and unpredictable environment. This development can be seen on both the market and supply side. To respond to these challenges, manufacturing companies must implement smart manufacturing systems and become more flexible and agile. The flexibility in operational planning regarding the scheduling and sequencing of customer orders needs to be increased and new structures must be implemented in manufacturing systems’ fundamental design as they constitute much of the operational flexibility available. To this end, smart and more flexible solutions for production planning and control (PPC) are developed. However, scheduling or sequencing is often only considered isolated in a predefined stable environment. Moreover, their orientation on the fundamental logic of the existing IT solutions and their applicability in a dynamic environment is limited. This paper presents a conceptual model for a task-based description logic that can be applied to factory planning, technology planning, and operational control. By using service-oriented architectures, the goal is to generate smart manufacturing systems. The logic is designed to allow for easy and automated maintenance. It is compatible with the existing resource and process allocation logic across operational and strategic factory and production planning. Full article

(This article belongs to the Special Issue Smart Industrial System)

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16 pages, 2959 KiB

Open AccessArticle

Research and Implementation of CPS for Transmission Front Middle Case Assembly Line

by Dianping Zhang, Xianfeng Cao, Zengzhi Jin, Yahui Zhang, Xiaofeng Hu and Chuanxun Wu

Appl. Sci. 2023, 13(10), 5912; https://doi.org/10.3390/app13105912 - 11 May 2023

Cited by 2 | Viewed by 1687

Abstract

As an indispensable part of the automobile factory transmission system, the transmission assembly line has a high level of automation and can play as a pioneer in the digital transformation of production. However, for the transmission production process, especially the front and middle case, there are many problems, such as a low degree of informatization, low efficiency of information transmission, and lack of a platform for rapid information release and sharing. This leads to the difficulty of timely discovery and feedback of exceptional events, resulting in low assembly efficiency. Therefore, this paper constructs its cyber–physical system (CPS) with the help of the Internet of Things (IoT) technologies, such as physical identification, information collection, and transmission. It contributes to improving the intelligent level of transmission of front and middle case assembly, strengthening the close integration, interaction, and collaboration between a large amount of assembly data and the physical entity of the assembly process, realizing the real-time monitoring of the transmission front and middle case assembly process, timely discovering, provide feedback, and dealing with exceptional events. In addition, scientific and effective adjustment strategies based on the data can be provided. Ultimately, the assembly efficiency of the transmission front and middle case is improved, ensuring the stability of the assembly line. Full article

(This article belongs to the Special Issue Smart Industrial System)

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16 pages, 12292 KiB

Open AccessArticle

AI-Based Intelligent Monitoring System for Estrus Prediction in the Livestock Industry

by Youngjoon Cho and Jongwon Kim

Appl. Sci. 2023, 13(4), 2442; https://doi.org/10.3390/app13042442 - 14 Feb 2023

Cited by 5 | Viewed by 2912

Abstract

In order to improve a livestock breeding environment that considers securing safe cattle resources and improving productivity for the intelligent farm, we propose an animal-friendly and worker-friendly intellectual monitoring system with Artificial Intelligent (AI) technology. In order to secure safe cattle resources and increase productivity for the livestock industry, it is necessary to secure the self-activities of the cattle and predict the estrous state of target cattle as quickly as possible. For the prediction of the estrous state, it is necessary to continuously observe the cattle behavior by workers and quantify the behavior of the target cattle, but that is not easy for workers and needs a long period of continuous observation. We developed the intelligent monitoring system (IMS) with the ARM (Augmented Recognition Model) for the intelligent farm that can predict the estrus of target cattle and get activity data for individual cattle, and then the system was applied to a typical cattle farm for activity monitoring of the Korean cattle (Hanwoo). Therefore, we confirmed the target Hanwoo group with more than 400 activities among the Hanwoo groups using the ARM threshold. Thus, we verified the potential of the proposed system for tracking multiple similar objects. Full article

(This article belongs to the Special Issue Smart Industrial System)

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28 pages, 754 KiB

Open AccessFeature PaperArticle

Investigation of Microservice-Based Workflow Management Solutions for Industrial Automation

by Jaime Garcia Represa, Felix Larrinaga, Pal Varga, William Ochoa, Alain Perez, Dániel Kozma and Jerker Delsing

Appl. Sci. 2023, 13(3), 1835; https://doi.org/10.3390/app13031835 - 31 Jan 2023

Cited by 5 | Viewed by 3326

Abstract

In an era ruled by data and information, engineers need new tools to cope with the increased complexity of industrial operations. New architectural models for industry enable open communication environments, where workflows can play a major role in providing flexible and dynamic interactions between systems. Workflows help engineers maintain precise control over their factory equipment and Information Technology (IT) services, from the initial design stages to plant operations. The current application of workflows departs from the classic business workflows that focus on office automation systems in favor of a manufacturing-oriented approach that involves direct interaction with cyber-physical systems (CPSs) on the shop floor. This paper identifies relevant industry-related challenges that hinder the adoption of workflow technology, which are classified within the context of a cohesive workflow lifecycle. The classification compares the various workflow management solutions and systems used to monitor and execute workflows. These solutions have been developed alongside the Eclipse Arrowhead framework, which provides a common infrastructure for designing systems according to the microservice architectural principles. This paper investigates and compares various solutions for workflow management and execution in light of the associated industrial requirements. Further, it compares various microservice-based approaches and their implementation. The objective is to support industrial stakeholders in their decision-making with regard to choosing among workflow management solutions. Full article

(This article belongs to the Special Issue Smart Industrial System)

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34 pages, 10096 KiB

Open AccessArticle

Maintenance 5.0: Towards a Worker-in-the-Loop Framework for Resilient Smart Manufacturing

by Alejandro Cortés-Leal, César Cárdenas and Carolina Del-Valle-Soto

Appl. Sci. 2022, 12(22), 11330; https://doi.org/10.3390/app122211330 - 8 Nov 2022

Cited by 21 | Viewed by 4518

Abstract

Due to the global uncertainty caused by social problems such as COVID-19 and the war in Ukraine, companies have opted for the use of emerging technologies, to produce more with fewer resources and thus maintain their productivity; that is why the market for wearable artificial intelligence (AI) and wireless sensor networks (WSNs) has grown exponentially. In the last decade, maintenance 4.0 has achieved best practices due to the appearance of emerging technologies that improve productivity. However, some social trends seek to explore the interaction of AI with human beings to solve these problems, such as Society 5.0 and Industry 5.0. The research question is: could a human-in-the-loop-based maintenance framework improve the resilience of physical assets? This work helps to answer this question through the following contributions: first, a search for research gaps in maintenance; second, a scoping literature review of the research question; third, the definition, characteristics, and the control cycle of Maintenance 5.0 framework; fourth, the maintenance worker 5.0 definition and characteristics; fifth, two proposals for the calculation of resilient maintenance; and finally, Maintenance 5.0 is validated through a simulation in which the use of the worker in the loop improves the resilience of an Industrial Wireless Sensor Network (IWSN). Full article

(This article belongs to the Special Issue Smart Industrial System)

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14 pages, 2466 KiB

Open AccessArticle

An ICS Traffic Classification Based on Industrial Control Protocol Keyword Feature Extraction Algorithm

by Changhong Yu, Ze Zhang and Ming Gao

Appl. Sci. 2022, 12(21), 11193; https://doi.org/10.3390/app122111193 - 4 Nov 2022

Cited by 4 | Viewed by 1785

Abstract

Industrial control protocol feature extraction is an important way to improve the accuracy and speed of industrial control protocol traffic classification. This paper firstly proposes a keyword feature extraction method for industrial control protocol, and then designs and implements an industrial control system (ICS) traffic classification based on this method. The proposed method utilizes the characteristics of the relatively fixed format of the industrial control protocol and the periodicity of the protocol traffic in ICS. The keyword features of the industrial control protocol can be accurately extracted after data preprocessing, data segmentation, redundant data filtering, and feature byte mining. A feature dataset is then formed. The designed ICS traffic classifier adopts decision tree and is trained with the feature dataset. Experiments are carried out on the open-source dataset. The results show that the proposed method achieves 99.99% classification accuracy, and the classification precision and classification recall rate reach 99.98% and 99.93%, respectively. The training time and predicting time of classifier are 0.34 s and 0.264 s, respectively, which meets the requirements of high precision and low latency of industrial control system. Full article

(This article belongs to the Special Issue Smart Industrial System)

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21 pages, 940 KiB

Open AccessArticle

Project Success Criteria Evaluation for a Project-Based Organization and Its Stakeholders—A Q-Methodology Approach

by Leonardo Sastoque-Pinilla, Sascha Artelt, Aleksandra Burimova, Norberto Lopez de Lacalle and Nerea Toledo-Gandarias

Appl. Sci. 2022, 12(21), 11090; https://doi.org/10.3390/app122111090 - 1 Nov 2022

Cited by 4 | Viewed by 9926

Abstract

The criteria that define project success change from one project to another, also from organization to organization, making success contextual for both the project organization and its stakeholders. This paper proposes a way to bridge this gap between what project success means to an organization and to its stakeholders in the context of Research and Development (R&D) projects. To achieve this, the available literature on project success has been analyzed to convert the different aspects identified into tangible units, allowing us to define and analyze the success criteria of a project in different dimensions. Subsequently, using Q-Methodology, which allowed us to determine among subjective opinions of Project Managers (PMs) of a project-based organization and their internal stakeholders, we will determine which criteria, within the previously identified dimensions, they consider as the most important for the success of a project, aiming to identify common success criteria that can be measured and controlled in the projects. Achieving the project goal, customer satisfaction regarding the quality of the activities, and knowledge generation turned out to be the most important criteria for PMs and stakeholders. Full article

(This article belongs to the Special Issue Smart Industrial System)

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22 pages, 1169 KiB

Open AccessArticle

Software-Defined Network-Based Energy-Aware Routing Method for Wireless Sensor Networks in Industry 4.0

by Sumayah Almuntasheri and Mohammed J. F. Alenazi

Appl. Sci. 2022, 12(19), 10073; https://doi.org/10.3390/app121910073 - 7 Oct 2022

Cited by 7 | Viewed by 2172

Abstract

Recent technological developments have led to the emergence of the next generation of industry—Industry 4.0. The Industrial Internet of Things (IIoT) is a key enabler of this new manufacturing paradigm where millions of interconnected smart devices, such as sensors and robots, manage massive amounts of data. Wireless sensor networks (WSNs), which allow the integration, flexibility, and scalability of the production line, thus avoiding the need for complex and expensive wired networks, are essential for IIoT. Nevertheless, the nonstop improvements of the smart industry have increased the amount of data transmitted by WSNs, making their nodes, which rely on small batteries, prone to exhaustion. In this scenario, where the transmission could be abruptly interrupted, losing time, information, and money, the development of energy-based management strategies for reducing the energy consumption of WSNs is urgent. In this paper, a software-defined network (SDN)-based energy-aware routing protocol is proposed to optimize the power consumption of WSNs within the framework of IIoT to support Industry 4.0. The SDN controller estimates the energy level of critical nodes in the WSN and decides the best routing path based on their energy consumption rather than on the widely used shortest-path criterion. Experimental results, obtained via a Mininet-Wifi simulation, show that the proposed approach prevents WSNs’ nodes from draining their batteries and abruptly interrupting the data transmission. Hence, valuable retransmission time is saved, potential information loss is prevented, the need for replacing the node’s battery is avoided, and the transmission lifetime is prolonged. In addition, the baseline shortest-path routing method is outperformed in terms of energy consumption and node failure, doubling its transmission time. Full article

(This article belongs to the Special Issue Smart Industrial System)

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23 pages, 3297 KiB

Open AccessArticle

A Maintenance Maturity Model for Assessing Information Management Practices for Small and Medium Enterprises (M³AIN4SME)

by Alessia Maria Rosaria Tortora, Valentina Di Pasquale and Raffaele Iannone

Appl. Sci. 2022, 12(18), 9282; https://doi.org/10.3390/app12189282 - 16 Sep 2022

Cited by 2 | Viewed by 3078

Abstract

Maintenance management is assuming an increasingly important role and garnering increased attention in Small and Medium Enterprises (SMEs). However, the difficulty of collecting data and processing information is evident in such contexts. In the current literature, few maintenance maturity models focus on the maintenance information management practices field. Moreover, though the existing models allow for assessing the maturity level, they do not indicate or assist in identifying and defining actions to reach the highest level. Furthermore, these models are not suitable for any type of organisation, as the assessment areas defined are quite generic (high level). For this reason, this paper proposes an innovative model for assessing the maturity level of maintenance management information practices in Small and Medium Enterprises (SMEs). The model provides the organisation with the strengths and weaknesses of their maintenance information management practices. The proposed model allows a clear measure of the maturity of the maintenance information management practices in smaller industrial contexts and provides a customised improvement programme. The model proposed supports small and medium companies to improve the effectiveness and efficiency of their maintenance management information infrastructure. The maturity model developed, in addition to being an assessment tool, provides and supports knowledge on the behaviours and practices for achieving world-class results. Full article

(This article belongs to the Special Issue Smart Industrial System)

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26 pages, 2363 KiB

Open AccessArticle

Identification Overview of Industry 4.0 Essential Attributes and Resource-Limited Embedded Artificial-Intelligence-of-Things Devices for Small and Medium-Sized Enterprises

by Martin Barton, Roman Budjac, Pavol Tanuska, Gabriel Gaspar and Peter Schreiber

Appl. Sci. 2022, 12(11), 5672; https://doi.org/10.3390/app12115672 - 2 Jun 2022

Cited by 21 | Viewed by 5022

Abstract

Nowadays there is a growing demand for small- and medium-sized enterprises (SMEs) to improve their level of digitalisation. This situation becomes even more critical in cases when SMEs act in the role of a subcontractor of large enterprises who demand the utilisation of certain digital operations. This paper aims to identify the essential Industry 4.0 attributes for the requirements of SMEs that enterprises can purchase to deploy an adequate solution with a view of increasing their competitiveness in the market. By analysing research articles and statistical data from the worldwide Web of Science database, we identify the major Industry 4.0 attributes for SME: Internet of Things (IoT), Big Data, Artificial Intelligence (AI), Cloud Computing, Simulation and Cybersecurity. Based on the review results and a survey by the European Commission, we propose devices primarily designed to implement AI tasks in industrial environments that meet the essential attributes for SMEs and have low entry costs. The subject of IoT is thoroughly addressed. Its subsets and the relationship between Industrial Internet of Things (IIoT) and Artificial Intelligence of Things (AIoT) are introduced and described. The characteristics of the listed devices as related to usability in the identified attributes are verified. Therefore, the description of the devices is provided with respect to their usability in SMEs. The main purpose of this paper is to identify attributes for SMEs and to develop strategic plans for the digitalisation requirements, particularly in the development of Artificial Intelligence as part of the implementation of the IoT pillar. Full article

(This article belongs to the Special Issue Smart Industrial System)

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24 pages, 6441 KiB

Open AccessEditor’s ChoiceReview

How Does the Metaverse Shape Education? A Systematic Literature Review

by Fabio De Felice, Antonella Petrillo, Gianfranco Iovine, Cinzia Salzano and Ilaria Baffo

Appl. Sci. 2023, 13(9), 5682; https://doi.org/10.3390/app13095682 - 5 May 2023

Cited by 31 | Viewed by 12086

Abstract

In recent years, the potential of the metaverse as a tool to connect people has been increasingly recognized. The opportunities offered by the metaverse seem enormous in many sectors and fields of application. However, on the academic side, although a growing number of papers have been found to address the adoption of the metaverse, a clear overview of the solutions in place and their impact on education has been largely neglected so far. In the context of increasing challenges found with the metaverse, this review aims to investigate the role of the metaverse as tool in education. This contribution aims to address this research gap by offering a state-of-the-art analysis of the role the metaverse plays in education in relation to the future of work. The study is based on a systematic review approach performed by means of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol. The findings of this research help us to better understand the benefits, potential and risks of the metaverse as a tool for immersive and innovative learning experiences. Implications are discussed and streams for future investigation are identified. Full article

(This article belongs to the Special Issue Smart Industrial System)

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