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Transition to Industry 4.0 in Emerging Domains: Methodology and Case Studies

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A project collection of Electronics (ISSN 2079-9292). This project collection belongs to the section "Computer Science & Engineering".

Papers displayed on this page all arise from the same project. Editorial decisions were made independently of project staff and handled by the Editor-in-Chief or qualified Editorial Board members.

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Editors

Dr. Anastasiia Rozhok

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Collection Editor

Department of Mechanical, Energy, Logistics Engineering and Engineering Management, University of Genoa, 16145 Genoa, Italy
Interests: complex modelling and simulation systems for the environmental, safety, and security fields, including autonomous security, risks, and Industry 4.0

Prof. Dr. Roberto Revetria

E-Mail Website
Collection Editor

Department of Mechanical, Energy, Management and Transport Engineering, University of Genoa, 16145 Genoa, Italy
Interests: simulation; operations management; mechanics of structures; industrial installations; safety

Project Overview

Dear Colleagues,

In the last decade, great importance has been given to the digital world and the ramping development of Industry 4.0. The possibility of employing new technologies able to recreate new environments aiming at simplifying and increasing the reliability of the real world has been the driving force for many new inventions and ideas. With augmented reality (AR), virtual reality (VR), and the Internet of Thing (IoT) and digital twin paradigms, the view of the world expanded, giving us the possibility of relating ourselves to the real world but with a previous digital simulation or digital enhancement. The possibility of developing technologies, tools and methodologies for the emerging domains of Industry 4.0 is the main reason why the reliability and safety of many systems increased during this period of time.

This Project Collection focuses on the recent advances in the digital technology field, in their implementation, and in their interoperability, with emphasis on the importance of these in the relevant domains, such as: public and private sectors, public administration, healthcare and defense, industrial sector, and the education sector.

Dr. Anastasiia Rozhok
Prof. Dr. Roberto Revetria
Collection Editors

This program intends to provide an opportunity for early career scientists to enhance their editing, networking, and organizational skills and to work closely with our journal to gain more editorial experience. Early career scientists who have novel ideas for new Special Issues of Electronics (ISSN: 2079-9292) will act as Guest Editors under the mentorship of an experienced scientist; this mentor could be a member of the Electronics Editorial Board or may be from other well-established research institutes or laboratories, etc.

Certificates and awards:

When the Special Issue is closed, the Editorial Office will provide official certificates for all of the mentors. The young scholars involved in the program will be prioritized as candidates for Electronics Young Investigator Awards in the future.

If you are interested in this opportunity, please send your Special Issue proposal to the Electronics Editorial Office (electronics@mdpi.com or ada.tian@mdpi.com), and we will discuss the process (mentor collaboration, Special Issue topic feasibility analysis, etc.) in further detail.

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 collection 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. Electronics 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

Industry 4.0
digital technologies
digitalization
modeling and simulation
AR
VR
IoT
digital twins
business process reengineering
interoperability
strategic planning
supply chain management
BIM
PLM

Published Papers (9 papers)

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2024

Jump to: 2023, 2022

21 pages, 10687 KiB

Open AccessArticle

A Digital Twin Platform Integrating Process Parameter Simulation Solution for Intelligent Manufacturing

by Haoran Wang, Zuoqing Yang, Quan Zhang, Qilei Sun and Enggee Lim

Electronics 2024, 13(4), 802; https://doi.org/10.3390/electronics13040802 - 19 Feb 2024

Viewed by 742

Abstract

The present work aims to develop a digital twin system typical of intelligent manufacturing applications, which has integrated visualization technologies, as well as the process parameter simulation solution. The application under consideration is a typical machining process, with a gantry machine tool controlled by Siemens Programmable Logic Controller(PLC) S7-1200. With the establishment of dual-directional data communication between the physical machine tool and its virtual counterpart based on TCP/IP protocol, real-time visualization, monitoring, and control of the entire working process can be achieved. Furthermore, we integrated with the digital twin system as a solution for real-time process parameter simulation based on finite element modeling (FEM), which enables the real-time monitoring of necessary process parameters, e.g., surface deformation, during the machining process. A preliminary experiment was conducted to validate our proposed digital twin system, and the results demonstrated that our proposed method has satisfactory performance in terms of both control and monitoring of the traditional machining process, and synchronization between the physical and virtual models is also proven to be positive with minimal latency. Full article

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2023

Jump to: 2024, 2022

17 pages, 2949 KiB

Open AccessArticle

A Digital Twin-Based Approach for the Optimization of Floor-Ball Manufacturing

by Tobias Gutmann, Felix Nyffenegger, Marco Pellegrini, Alessandro Cabrucci and Alessandro Guzzini

Electronics 2023, 12(24), 4979; https://doi.org/10.3390/electronics12244979 - 12 Dec 2023

Cited by 1 | Viewed by 800

Abstract

The increasing complexity of products and manufacturing processes, combined with the constantly advancing technological integration of the manufacturing sector, raised new challenges for world-class industries to optimize time-to-market, resources, and cost. Simulation, as an essential Industry 4.0 enabling technology, allows one to emulate the steps of a manufacturing process, thereby achieving significant improvements in all the product and process development phases. A simulation process can be implemented and improved by creating the Digital Twin of the manufacturing system, which can be realized on a single-line scale or extended to the whole factory. The Digital Twin merges physics-based system modeling and real-time process data to generate a virtual copy of an observable object to reduce and optimize the extensive time and cost of physical design, prototyping, commissioning, reconfiguration, and maintenance. This study aims to investigate how the implementation of digital twin technology can help optimize the balance between power consumption and productivity, taking into account existing barriers and limitations. By following this outline, this study shows the design and development of a digital twin for a floor-ball manufacturing line present in the Smart Factory of Ostschweizer Fachhochschule (Switzerland). The entire production process is reproduced with Siemens Technomatix Plant Simulation software 2201, and data connection and processing are handled by a tailored toolchain consisting of an agent, a database, Python packages, and the COM interface from Tecnomatix. This toolchain feeds the digital twin with data from the physical operating environment. In particular, this study compares direct power measurements with the ones expected by the digital twin to assess digital model accuracy. Full article

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16 pages, 2486 KiB

Open AccessArticle

“Canalvoltaico” in Emilia-Romagna, Italy: Assessing Technical, Economic, and Environmental Feasibility of Suspended Photovoltaic Panels over Water Canals

by Valentino Solfrini, Riccardo Farneti, Jessica Rossi, Augusto Bianchini, Matteo Morolli and Ivan Savini

Electronics 2023, 12(23), 4879; https://doi.org/10.3390/electronics12234879 - 04 Dec 2023

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Abstract

Solar energy has become an increasingly important part of the global energy mix. In Italy, the photovoltaic power installed has grown by 40% since 2015, which raises the issue of land use and occupation. A viable alternative, already experienced in India, is placing solar panels on the top of water canals (Canal-Top—in Italian, “Canalvoltaico”). It is a relatively new and innovative approach to solar energy installation that offers several advantages including the potential to generate renewable energy without occupying additional land, reduce water evaporation from canals, and improve water quality by reducing algae growth. The article explores various Canal-Top solar projects over the world; then, a feasible application in the Italian region “Emilia-Romagna” is discussed, evaluating two potential construction designs. The primary aim is to establish a capital expenditure cost framework, offering reference values currently lacking in the extant literature and industry studies pertaining to Italy. Moreover, the study addresses additional key factors, including water savings, maintenance considerations, and safety implications. Full article

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15 pages, 1859 KiB

Open AccessSystematic Review

Artificial Intelligence to Solve Production Scheduling Problems in Real Industrial Settings: Systematic Literature Review

by Mateo Del Gallo, Giovanni Mazzuto, Filippo Emanuele Ciarapica and Maurizio Bevilacqua

Electronics 2023, 12(23), 4732; https://doi.org/10.3390/electronics12234732 - 22 Nov 2023

Cited by 4 | Viewed by 2598

Abstract

This literature review examines the increasing use of artificial intelligence (AI) in manufacturing systems, in line with the principles of Industry 4.0 and the growth of smart factories. AI is essential for managing the complexities in modern manufacturing, including machine failures, variable orders, and unpredictable work arrivals. This study, conducted using Scopus and Web of Science databases and bibliometric tools, has two main objectives. First, it identifies trends in AI-based scheduling solutions and the most common AI techniques. Second, it assesses the real impact of AI on production scheduling in real industrial settings. This study shows that particle swarm optimization, neural networks, and reinforcement learning are the most widely used techniques to solve scheduling problems. AI solutions have reduced production costs, increased energy efficiency, and improved scheduling in practical applications. AI is increasingly critical in addressing the evolving challenges in contemporary manufacturing environments. Full article

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13 pages, 6482 KiB

Open AccessArticle

Development of an Algorithm for Calculating the Moisture Content and Time of Forest Fire Maturation of Forest Combustible Materials for Determining Forest Fire Hazards

by Anatoliy A. Aleksandrov, Boris S. Ksenofontov, Alexey S. Kozodaev, Roman A. Taranov, Victoriya D. Vyazova and Mikhail V. Ivanov

Electronics 2023, 12(8), 1937; https://doi.org/10.3390/electronics12081937 - 20 Apr 2023

Cited by 4 | Viewed by 1047

Abstract

Nowadays, forests play an important role in stabilizing the ecological balance, being one of the most important components of the biosphere. Due to the vital activity of forests, the gas composition of the atmosphere is normalized. Mass forest fires have the opposite effect. They cause irreparable damage to flora and fauna, contribute to the melting of Arctic ice, an increase in the Earth’s temperature, and destabilization of the carbon balance. The purpose of this study is to develop an algorithm for calculating the moisture content and time of forest fire maturation of forest combustible materials. To achieve this goal, the main factors determining a forest fire hazard have been studied, as well as a review of existing methods for assessing forest fire danger and scientific papers on forest pyrology. As a result of the analysis of the research aimed at studying the rate of drying of forest combustible materials (FCM), depending on the physical properties and environmental parameters, a dependency of changes in moisture content over time was obtained. With its help, knowing the initial moisture content of FCM, it is possible to calculate the periods of fire maturation for each component of the forest plantation. Cooperative use of the resulting algorithm with a digital twin of a forest stand makes it possible to identify the most fire-hazard forest areas and estimate the period of their fire-prone maturation. Full article

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15 pages, 2576 KiB

Open AccessArticle

Human Factor Interrelationships to Improve Worker Reliability: Implementation of MCDM in the Agri-Food Sector

by Concetta Manuela La Fata, Antonio Giallanza, Luca Adelfio, Rosa Micale and Giada La Scalia

Electronics 2023, 12(2), 283; https://doi.org/10.3390/electronics12020283 - 05 Jan 2023

Cited by 3 | Viewed by 1021

Abstract

Performance Shaping Factors (PSFs) are contextual, individual, and cognitive factors used in Human Reliability Analysis (HRA) to quantify the worker contribution to errors when performing a generic task. Although the empirical evidence demonstrates the existence of PSF interrelationships, the majority of HRA methods assume their independence. As a consequence, the resulting Human Error Probability (HEP) might be over- or underestimated. To deal with this issue, only a few qualitative guidelines or statistical-based approaches have been proposed so far. While the former are not well structured, the latter require a high computational effort and a proper number of input data. Therefore, the present paper provides an alternative approach to deal with the PSFs interaction issue to facilitate the identification of the most influential human factors on which to take corrective actions. To this purpose, Multi Criteria Decision Making (MCDM) methods may represent a structured, effortless, and easily replicable framework. Owing to their ability to deal with the interdependence of decision factors, DEMATEL and ANP are hence considered and afterwards compared, highlighting their strengths and weaknesses. Both methods are implemented in an agri-food company which produces pistachios in Southern Italy. Full article

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13 pages, 280 KiB

Open AccessArticle

A Study on a Probabilistic Method for Designing Artificial Neural Networks for the Formation of Intelligent Technology Assemblies with High Variability

by Vladimir V. Bukhtoyarov, Vadim S. Tynchenko, Vladimir A. Nelyub, Igor S. Masich, Aleksey S. Borodulin and Andrei P. Gantimurov

Electronics 2023, 12(1), 215; https://doi.org/10.3390/electronics12010215 - 01 Jan 2023

Cited by 25 | Viewed by 1311

Abstract

Currently, ensemble approaches based, among other things, on the use of non-network models are powerful tools for solving data analysis problems in various practical applications. An important problem in the formation of ensembles of models is ensuring the synergy of solutions by using the properties of a variety of basic individual solutions; therefore, the problem of developing an approach that ensures the maintenance of diversity in a preliminary pool of models for an ensemble is relevant for development and research. This article is devoted to the study of the possibility of using a method for the probabilistic formation of neural network structures developed by the authors. In order to form ensembles of neural networks, the influence of parameters of neural network structure generation on the quality of solving regression problems is considered. To improve the quality of the overall ensemble solution, using a flexible adjustment of the probabilistic procedure for choosing the type of activation function when filling in the layers of a neural network is proposed. In order to determine the effectiveness of this approach, a number of numerical studies on the effectiveness of using neural network ensembles on a set of generated test tasks and real datasets were conducted. The procedure of forming a common solution in ensembles of neural networks based on the application of an evolutionary method of genetic programming is also considered. This article presents the results of a numerical study that demonstrate a higher efficiency of the approach with a modified structure formation procedure compared to a basic approach of selecting the best individual neural networks from a preformed pool. These numerical studies were carried out on a set of test problems and several problems with real datasets that, in particular, describe the process of ore-thermal melting. Full article

2022

Jump to: 2024, 2023

16 pages, 1180 KiB

Open AccessArticle

Human-Centered Design for Productivity and Safety in Collaborative Robots Cells: A New Methodological Approach

by Giovanni Boschetti, Maurizio Faccio and Irene Granata

Electronics 2023, 12(1), 167; https://doi.org/10.3390/electronics12010167 - 30 Dec 2022

Cited by 3 | Viewed by 2605

Abstract

Nowadays, the current market trend is oriented toward increasing mass customization, meaning that modern production systems have to be able to be flexible but also highly productive. This is due to the fact that we are still living in the so-called Industry 4.0, with its cornerstone of high-productivity systems. However, there is also a migration toward Industry 5.0 that includes the human-centered design of the workplace as one of its principles. This means that the operators have to be put in the center of the design techniques in order to maximize their wellness. Among the wide set of new technologies, collaborative robots (cobots) represent one such technology that modern production systems are trying to integrate, because of their characteristic of working directly with the human operators, allowing for a mix of the flexibility of the manual systems with the productivity of the automated ones. This paper focuses on the impact that these technologies have on different levels within a production plant and on the improvement of the collaborative experience. At the workstation level, the control methodologies are investigated and developed: technologies such as computer vision and augmented reality can be applied to aid and guide the activities of the cobot, in order to obtain the following results. The first is an increase in the overall productivity generated by the reduction of idle times and safety stops and the minimization of the effort required to the operator during the work. This can be achieved through a multiobjective task allocation which aims to simultaneoulsy minimize the makespan, for productivity requirements, and the operator’s energy expenditure and mental workload, for wellness requirements. The second is a safe, human-centered, workspace in which collisions can be avoided in real time. This can be achieved by using real-time multicamera systems and skeleton tracking to constantly know where the operator is in the work cell. The system will offer the possibility of directing feedback based on the discrepancies between the physical world and the virtual models in order to dynamically reallocate the tasks to the resources if the requirements are not satisfied anymore. This allows the application of the technology to sectors that require constant process control, improving also the human–robot interaction: the human operator and the cobot are not merely two single resources working in the same cell, but they can achieve a real human–robot collaboration. In this paper, a framework is preented that allows us to reach the different aforementioned goals. Full article

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25 pages, 5334 KiB

Open AccessArticle

Prediction of Critical Filling of a Storage Area Network by Machine Learning Methods

by Igor S. Masich, Vadim S. Tynchenko, Vladimir A. Nelyub, Vladimir V. Bukhtoyarov, Sergei O. Kurashkin, Andrei P. Gantimurov and Aleksey S. Borodulin

Electronics 2022, 11(24), 4150; https://doi.org/10.3390/electronics11244150 - 12 Dec 2022

Cited by 21 | Viewed by 1390

Abstract

The introduction of digital technologies into the activities of companies is based on software and hardware systems, which must function reliably and without interruption. The forecasting of the completion of storage area networks (SAN) is an essential tool for ensuring the smooth operation of such systems. The aim of this study is to develop a system of the modelling and simulation of the further loading of SAN on previously observed load measurements. The system is based on machine learning applied to the load prediction problem. Its novelty relates to the method used for forming input attributes to solve the machine learning problem. The proposed method is based on the aggregation of data on observed loading measurements and the formalization of the problem in the form of a regression analysis problem. The artificial dataset, synthesized stochastically according to the given parameter intervals and simulating SAN behavior, allowed for more extensive experimentation. The most effective algorithm is CatBoost (gradient boosting on decision trees), which surpasses other regression analysis algorithms in terms of R2 scores and MAE. The selection of the most significant features allows for the simplification of the prediction model with virtually no loss of accuracy, thereby reducing the number of confessions used. The experiments show that the proposed prediction model is adequate to the situation under consideration and allows for the prediction of the SAN load for the planning period under review with an R2 value greater than 0.9. The model has been validated on a series of real data on SAN. Full article

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Transition to Industry 4.0 in Emerging Domains: Methodology and Case Studies

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Project Overview

Keywords

Published Papers (9 papers)

2024

Jump to: 2023, 2022

2023

Jump to: 2024, 2022

2022

Jump to: 2024, 2023

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