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Processes
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Manufacturing Industry 4.0: Trends and Perspectives
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Manufacturing Industry 4.0: Trends and Perspectives

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 40707

Special Issue Editors

Prof. Dr. Bernard Riera

E-Mail Website
Guest Editor
CReSTIC, Reims Champagne-Ardenne University, 51687 Reims, France
Interests: Industry 4.0; manufacturing systems; logic controllers; human–machine systems; control education
Dr. Nadhir Messai

E-Mail Website
Guest Editor
CReSTIC, Reims Champagne-Ardenne University, 51687 Reims, France
Interests: control; safety and security; robotics

Special Issue Information

Dear Colleagues,

The Factory of the Future is a generic concept that is part of a general awareness of the importance of the manufacturing industry for nations’ development. This reflection is intended to maintain and develop a strong industry, generating wealth and new jobs. Hence, the Factory of the Future has to take into account several simultaneous transitions: energy, ecological, digital, organizational, and societal. Factories have to transform themselves to become a more sustainable industry and more respectful of the Earth. Today, the massive use of digital and information technologies such as cyberphysical systems, the Internet of Things (IoT), machine to machine (M2M) communication, big data, and cloud computing represent what it is called the fourth industrial revolution. The names are different all over the world: “Industry 4.0”, “Internet Factory”, “Smart Plant”, “Digital Factory”, “Integrated Industry”, “Innovative Factory”, “Intelligent Manufacturing”, “e-Factory”, or “Advanced Manufacturing”, but the concepts are the same. The convergence of the virtual world of the internet and IT (Information Technology) and the real world of industrial installations and OT (Operational Technology) will be the challenge for the Factory of the Future. Modern information and communication technologies seem a solution to increase productivity, quality, and flexibility within the industry. Hence, the industry has entered a phase of big change that sees digital technologies as a key factor for the future to design cyberphysical production systems. These systems are predicted to enable new automation paradigms and improve plant operations in terms of increased facilities effectiveness. The challenges are numerous, and this list is not exhaustive: connectivity and interoperability, virtualization, decentralization, innovative production lines and logistics, human-centered automation, etc.

This Special Issue on “Industry 4.0 : Trends and Perspectives” intends to present some methologies, tools, and applications for Industry 4.0. Topics include but are not limited to:

  • Smart manufacturing;
  • Cyberphysical systems;
  • Digital twins;
  • Cybersecurity and safety;
  • Virtual commissioning;
  • IIoT;
  • Smart robotics;
  • Maintenance 4.0;
  • Education 4.0.

Prof. Dr. Bernard Riera
Dr. Nadhir Messai
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. Processes 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 4.0
  • smart manufacturing
  • CPS
  • digital twins
  • robotics’ maintenance

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Published Papers (9 papers)

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Research

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25 pages, 7148 KiB  
Article
A Fault-Tolerant and Reconfigurable Control Framework: Modeling, Design, and Synthesis
by Imane Tahiri, Alexandre Philippot, Véronique Carré-Ménétrier and Bernard Riera
Processes 2023, 11(3), 701; https://doi.org/10.3390/pr11030701 - 26 Feb 2023
Viewed by 1851
Abstract
Manufacturing systems (MS) have become increasingly complex due to constraints induced by a changing environment, such as flexibility, availability, competition, and key performance indicators. This change has led to a need for flexible systems capable of adapting to production changes while meeting productivity [...] Read more.
Manufacturing systems (MS) have become increasingly complex due to constraints induced by a changing environment, such as flexibility, availability, competition, and key performance indicators. This change has led to a need for flexible systems capable of adapting to production changes while meeting productivity and quality criteria and reducing the risk of failures. This paper provides a methodology for designing reconfigurable and fault-tolerant control for implementation in a Programmable Logic Controller (PLC). The main contribution of this methodology is based on a safe control synthesis founded on timed properties. If a sensor fault is detected, the controller switches from normal behavior to a degraded one, where timed information replaces the information lost from the faulty sensor. Switching between normal and degraded behaviors is ensured through reconfiguration rules. The primary objective of this method is to implement the obtained control into a PLC. In order to achieve this goal, a method is proposed to translate the controllers of the two behaving modes and the reconfiguration rules into different Grafcets. This approach relies on the modular architecture of manufacturing systems to avoid the combinatorial explosion that occurs in several approaches. Full article
(This article belongs to the Special Issue Manufacturing Industry 4.0: Trends and Perspectives)
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17 pages, 3653 KiB  
Article
Fresh Approaches for Structured Text Programmable Logic Controllers Programs Verification
by Émile Siboulet, Louen Pottier, Tom Ranger and Bernard Riera
Processes 2023, 11(3), 687; https://doi.org/10.3390/pr11030687 - 24 Feb 2023
Viewed by 1812
Abstract
Programmable logic controllers (PLCs) are everywhere today and perform critical tasks in industries. They are considered as a key component for the Industry 4.0. Before they are put into operation, it is necessary to check the accuracy of the PLC programs. This verification [...] Read more.
Programmable logic controllers (PLCs) are everywhere today and perform critical tasks in industries. They are considered as a key component for the Industry 4.0. Before they are put into operation, it is necessary to check the accuracy of the PLC programs. This verification operation can be performed using model checkers. This stage is often long and costly and requires a domain expert who can understand the system, as well as the different model checker tools able to verify the code implemented in the controller. Furthermore, this verification often requires a conversion of the PLC code into a language understood by a model checker which can influence the behavior of the observed PLC. Hence, there is a need to propose methods and tools which could be used by technicians and engineers. The aim of this paper is to propose methods that require little work to set up and are robust to program sizes used in Industry 4.0. This paper explores some fresh ideas for human-adapted PLC code verification. We present different methods to test codes in structured text (ST) compliant with the IEC 61131-3 standard. Hence, the first idea is to test the ST code that will be directly implemented on a controller. For that, we propose a method using the model checker UPPAAL which allows us to obtain exact results on short codes. Second, we propose verifying the generic properties that a PLC program must avoid: deadlocks, non-accessible states and fugitive states or actions. To solve combinatory explosion problems encountered with the UPPAAL software, the third proposition consists of using relational databases. The same verification as previously followed can be obtained, but the search time is longer. The fourth and last proposal is to process the ST code with a neural network composed of long short-term memory layers (LSTM) to quickly determine the validity of the code. This method could give an approximation of code errors in a few seconds. The different proposed methods are supported with several examples. Full article
(This article belongs to the Special Issue Manufacturing Industry 4.0: Trends and Perspectives)
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22 pages, 666 KiB  
Article
Robustness Evaluation Process for Scheduling under Uncertainties
by Sara Himmiche, Pascale Marangé, Alexis Aubry and Jean-François Pétin
Processes 2023, 11(2), 371; https://doi.org/10.3390/pr11020371 - 25 Jan 2023
Cited by 1 | Viewed by 1807
Abstract
Scheduling production is an important decision issue in the manufacturing domain. With the advent of the era of Industry 4.0, the basic generation of schedules becomes no longer sufficient to face the new constraints of flexibility and agility that characterize the new architecture [...] Read more.
Scheduling production is an important decision issue in the manufacturing domain. With the advent of the era of Industry 4.0, the basic generation of schedules becomes no longer sufficient to face the new constraints of flexibility and agility that characterize the new architecture of production systems. In this context, schedules must take into account an increasingly disrupted environment while maintaining a good performance level. This paper contributes to the identified field of smart manufacturing scheduling by proposing a complete process for assessing the robustness of schedule solutions: i.e., its ability to resist to uncertainties. This process focuses on helping the decision maker in choosing the best scheduling strategy to be implemented. It aims at considering the impact of uncertainties on the robustness performance of predictive schedules. Moreover, it is assumed that data upcoming from connected workshops are available, such that uncertainties can be identified and modelled by stochastic variables This process is supported by stochastic timed automata for modelling these uncertainties. The proposed approach is thus based on Stochastic Discrete Event Systems models and model checking techniques defining a highly reusable and modular process. The solution process is illustrated on an academic example and its performance (generecity and scalability) are deeply evaluated using statistical analysis. The proposed application of the evaluation process is based on the technological opportunities offered by the Industry 4.0. Full article
(This article belongs to the Special Issue Manufacturing Industry 4.0: Trends and Perspectives)
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20 pages, 702 KiB  
Article
Hierarchical Deep LSTM for Fault Detection and Diagnosis for a Chemical Process
by Piyush Agarwal, Jorge Ivan Mireles Gonzalez, Ali Elkamel and Hector Budman
Processes 2022, 10(12), 2557; https://doi.org/10.3390/pr10122557 - 1 Dec 2022
Cited by 12 | Viewed by 2342
Abstract
A hierarchical structure based on a Deep LSTM Supervised Autoencoder Neural Network (Deep LSTM-SAE NN) is presented for the detection and classification of faults in industrial plants. The proposed methodology has the ability to classify incipient faults that are difficult to detect and [...] Read more.
A hierarchical structure based on a Deep LSTM Supervised Autoencoder Neural Network (Deep LSTM-SAE NN) is presented for the detection and classification of faults in industrial plants. The proposed methodology has the ability to classify incipient faults that are difficult to detect and diagnose with traditional and many recent methods. Faults are grouped into different subsets according to the degree of difficulty to classify them accurately in the proposed hierarchical structure. External pseudo-random binary signals (PRBS) are injected in the system to enhance the identification of incipient faults. The approach is illustrated on the benchmark process (Tennessee Eastman Process) in order to compare across different methodologies. The efficacy of the proposed method is shown by a comprehensive comparison between many recent and traditional fault detection and diagnosis methods in the literature for Tennessee Eastman Process. The proposed work results in significant improvements in the classification of faults over both multivariate linear model-based strategies and non-hierarchical nonlinear model-based strategies. Full article
(This article belongs to the Special Issue Manufacturing Industry 4.0: Trends and Perspectives)
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23 pages, 3555 KiB  
Article
The Academic Development Trajectories of the Lean Production Based on Main Path Analysis Method
by Pi-Yu Lin, Kai-Ying Chen, Chen-Yang Cheng, Wei-Hao Su and Louis Y. Y. Lu
Processes 2022, 10(8), 1495; https://doi.org/10.3390/pr10081495 - 29 Jul 2022
Cited by 1 | Viewed by 2381
Abstract
Enterprises looking to be competitive are constantly looking for a continuous increase in productivity, quality, and level of services. With the development of the industry 4.0 concept, manufacturers are more confident about the new advantages of automation and systems integration. Lean management is [...] Read more.
Enterprises looking to be competitive are constantly looking for a continuous increase in productivity, quality, and level of services. With the development of the industry 4.0 concept, manufacturers are more confident about the new advantages of automation and systems integration. Lean management is a well-developed and empirically proven managerial strategy. Combining lean and industry 4.0 practices seems to be a necessary evolutionary step to further raise the level of operational excellence. This study applied the main path analysis method to explore the development trend of lean management in the academic field. First, this study adopted the Scopus database to collect relevant papers, then analyzed their overall development trajectory by using Main Path 437 software, and used the g-index and h-index to identify more influential journals. Next, this study clustered the papers with similar topics into several groups, and then used Wordle software to present the keywords of each group in a word cloud that serves as a reference for naming. Thus, the top five groups obtained are as follows: “Lean production concept and application”, “Lean Six Sigma concept and application”, “Lean system integration and application”, “Lean construction concept and application”, and “Lean healthcare concept and application”. Finally, this study provides explanations and conclusions on each group’s development trajectory, as well as research recommendations in the field of lean production. The findings can serve as guides for industry, government, and academia as they develop future lean production development strategies. This study utilized an integrated analysis approach to successfully and effectively depict the trajectory of lean production development and applications, identify future research and development directions, and generate technological forecasts. Full article
(This article belongs to the Special Issue Manufacturing Industry 4.0: Trends and Perspectives)
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16 pages, 3826 KiB  
Article
Inline Weld Depth Evaluation and Control Based on OCT Keyhole Depth Measurement and Fuzzy Control
by Maximilian Schmoeller, Tony Weiss, Korbinian Goetz, Christian Stadter, Christian Bernauer and Michael F. Zaeh
Processes 2022, 10(7), 1422; https://doi.org/10.3390/pr10071422 - 21 Jul 2022
Cited by 8 | Viewed by 2490
Abstract
In an industrial joining process, exemplified by deep penetration laser beam welding, ensuring a high quality of welds requires a great effort. The quality cannot be fully established by testing, but can only be produced. The fundamental requirements for a high weld seam [...] Read more.
In an industrial joining process, exemplified by deep penetration laser beam welding, ensuring a high quality of welds requires a great effort. The quality cannot be fully established by testing, but can only be produced. The fundamental requirements for a high weld seam quality in laser beam welding are therefore already laid in the process, which makes the use of control systems essential in fully automated production. With the aid of process monitoring systems that can supply data inline to a production process, the foundation is laid for the efficient and cycle-time-neutral control of welding processes. In particular, if novel, direct measurement methods, such as Optical Coherence Tomography, are used for the acquisition of direct geometric quantities, e.g., the weld penetration depth, a significant control potential can be exploited. In this work, an inline weld depth control system based on an OCT keyhole depth measurement is presented. The system is capable of automatically executing an inline control of the deep penetration welding process based only on a specified target weld depth. The performance of the control system was demonstrated on various aluminum alloys and for different penetration depths. In addition, the ability of the control to respond to unforeseen external disturbances was tested. Within the scope of this work, it was thus possible to provide an outlook on future developments in the field of laser welding technology, which could develop in the direction of an intuitive manufacturing process. This objective should be accomplished through the use of intelligent algorithms and innovative measurement technology—following the example of laser beam cutting, where the processing systems themselves have been provided with the ability to select suitable process parameters for several years now. Full article
(This article belongs to the Special Issue Manufacturing Industry 4.0: Trends and Perspectives)
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21 pages, 3419 KiB  
Article
Development of a Sustainable Industry 4.0 Approach for Increasing the Performance of SMEs
by Paul-Eric Dossou, Gaspard Laouénan and Jean-Yves Didier
Processes 2022, 10(6), 1092; https://doi.org/10.3390/pr10061092 - 30 May 2022
Cited by 19 | Viewed by 3659
Abstract
The competitiveness of companies in emerging countries implies many European countries must transform their production systems to be more efficient. Indeed, the new context created by the COVID-19 pandemic increases the necessity of digital transformation and focuses attention on its limited uptake by [...] Read more.
The competitiveness of companies in emerging countries implies many European countries must transform their production systems to be more efficient. Indeed, the new context created by the COVID-19 pandemic increases the necessity of digital transformation and focuses attention on its limited uptake by manufacturing companies. In France, the Industry 4.0 concepts are already implemented in large companies. Despite the demonstration and validation of their benefits, SMEs are reluctant to move towards implementation. This problem of SME performance improvement increases with the current geopolitical situation in Europe (raw materials and gasoil cost). It is thus urgent and paramount to find a better solution for encouraging SMEs in their transformation. Taking note of the brakes on uptake of Industry 4.0 concepts in SMEs, the objectives of this paper are to find levers to accelerate implementation of Industry 4.0 concepts in SMEs, through the development and the deployment of a sustainable Industry 4.0 methodology, and to develop an intelligent system for supporting companies’ digital transformation in order to improve their performance. After a literature review, focused on Industry 4.0 concepts, theory of systems, organizational methods, and artificial intelligence, a sustainable methodology will be presented. The SME performance model that has been elaborated will then be shown and the structure of the intelligent system (mainly the decision aided tool) being developed for supporting the digital transformation of SMEs will be described. An illustrative example relating to a food elaboration SME will be presented for validating the concepts that have been developed. The proposed framework helped the company to formulate guidelines and transition towards a sustainable 4.0 company. Full article
(This article belongs to the Special Issue Manufacturing Industry 4.0: Trends and Perspectives)
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30 pages, 1064 KiB  
Article
The Product Customization Process in Relation to Industry 4.0 and Digitalization
by Martin Pech and Jaroslav Vrchota
Processes 2022, 10(3), 539; https://doi.org/10.3390/pr10030539 - 9 Mar 2022
Cited by 31 | Viewed by 15820
Abstract
Today’s customer no longer wants one-size-fits-all products but expects products and services to be as tailored as possible. Mass customization and personalization are becoming a trend in the digitalization strategy of enterprises and manufacturing in Industry 4.0. The purpose of the paper is [...] Read more.
Today’s customer no longer wants one-size-fits-all products but expects products and services to be as tailored as possible. Mass customization and personalization are becoming a trend in the digitalization strategy of enterprises and manufacturing in Industry 4.0. The purpose of the paper is to develop and validate a conceptual model for leveraging Industry 4.0 and digitalization to support product customization. We explored the implications and impacts of Industry 4.0 and digitalization on product customization processes and determine the importance of variables. We applied structural equation modeling (SEM) to test our hypotheses regarding the antecedents and consequences of digitalization and Industry 4.0. We estimated the process model using the partial least squares (PLS) method, and goodness of fit measures show acceptable values. The proposed model considers relationships between technology readiness, digitalization, internal and external integration, internal value chain, and customization. The results show the importance of digitalization and technology readiness for product customization. The results reveal that the variable of internal integration plays a crucial mediating role in applying new technologies and digitalization for customization. The paper’s main contribution is the conclusion that, for successful implementation of the customization process, models are required to focus on the internal and external factors of the business environment. Our findings are supported by various practical applications of possible product customization. Full article
(This article belongs to the Special Issue Manufacturing Industry 4.0: Trends and Perspectives)
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Review

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27 pages, 2039 KiB  
Review
Integration of Maintenance Management System Functions with Industry 4.0 Technologies and Features—A Review
by Basheer Wasef Shaheen and István Németh
Processes 2022, 10(11), 2173; https://doi.org/10.3390/pr10112173 - 24 Oct 2022
Cited by 29 | Viewed by 7300
Abstract
Industry 4.0 is the latest technological age, in which recent technological developments are being integrated within industrial systems. Consequently, maintenance management of current industrial manufacturing systems is affected by the emergence of the technologies and features of Industry 4.0. This study aimed to [...] Read more.
Industry 4.0 is the latest technological age, in which recent technological developments are being integrated within industrial systems. Consequently, maintenance management of current industrial manufacturing systems is affected by the emergence of the technologies and features of Industry 4.0. This study aimed to conduct a comprehensive literature review to understand how Industry 4.0 technologies and features affect the various functions of maintenance management systems. The reviewing process was initiated by examining the most recent related literature in three different databases. In total, 54 articles were classified into three research categories. Then, the integration of the main functions and components of the adopted maintenance management model and the Industry 4.0 features and technologies were aligned, focusing on the driving force of predictive maintenance. The analysis focused mainly on the technical aspects of the integration process, including integration concepts and integration-assisting tools, identifying the main applications and highlighting the challenges identified in the analysed literature. The key findings were that the main functions of maintenance management systems are significantly influenced by different Industry 4.0 technologies, mainly artificial intelligence–machine learning, CPS, IoT, big data, augmented reality, and cloud computing, in terms of successful integration. Consequently, the overall system implied tangible improvements through the involvement of different Industry 4.0 features which promote real-time condition monitoring, enable data management and curation, increase coordination between various maintenance tasks, facilitate supervision through remote maintenance applications, and, overall, improve operations and productivity, reduce unplanned shutdowns and, as a result, reduce the associated costs. To provide research directions, examples, and methodologies for integrating the various maintenance management system functions with the cutting-edge Industry 4.0 technologies and features based on real and practical cases present in the reviewed literature, the review’s findings are comprehensively categorised and summarised. Full article
(This article belongs to the Special Issue Manufacturing Industry 4.0: Trends and Perspectives)
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