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Processes
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Digitalized Industrial Production Systems and Industry 4.0, Volume II
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Digitalized Industrial Production Systems and Industry 4.0, Volume II

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Advanced Digital and Other Processes".

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 17608

Special Issue Editors

Prof. Dr. José Barbosa

E-Mail Website
Guest Editor
Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal
Interests: cyber-physical systems; internet of things; multi-agent systems; holonic manufacturing systems; self-organization
Special Issues, Collections and Topics in MDPI journals
Dr. Luis Ribeiro

E-Mail Website
Guest Editor
Associate Professor, Department Department of Managing and Engineering, Division of Manufacturing Engineering, Linköping University, 581 83 Linköping, Sweden
Interests: cyber-physical systems; automation; manufacturing; agents and multiagent systems; service oriented architectures
Prof. Dr. Paulo Leitao

E-Mail Website
Guest Editor
Research Centre in Digitalization and Intelligent Robotics (CeDRI), Polytechnic Institute of Bragança, 5300-252 Bragança, Portugal
Interests: intelligent and reconfigurable systems; cyber-physical systems; multi-agent systems; internet of things; factory automation; holonic systems; self-organized systems

Special Issue Information

Dear Colleagues,

We are currently running a Special Issue entitled “Digitalized Industrial Production Systems and Industry 4.0, Volume II”, to be published in the open access journal Processes. All of the papers may be submitted immediately or at any point until this deadline as papers will be published on an ongoing basis.

The present Special Issue aims to present up-to-date information on recent scientific advances in the digitalization of industrial production systems and in the application of Industry 4.0 concepts. Real applications of the present topics are highly encouraged, as they would greatly inspire future application of innovative research.

Papers dealing with the following topics are especially sought (although the Special Issue is not strictly limited to these):

  • Industrial CPS and smart manufacturing;
  • Architecture design and analysis;
  • Industrial IoT and factory of things and Internet of Things;
  • Modeling and control for cyberphysical systems;
  • Edge computing, fog computing, and IoT/IoE;
  • Machine-to-machine (M2M)/device-to-device communications and IoT/IoE;
  • Cloud-IoT/IoE cyberphysical systems;
  • Cyberphysical system architectures;
  • Human factors and humans in the loop in CPS;
  • The role of CPS in Industry 4.0;
  • The role of the IoT in Industry 4.0;
  • The role of industrial agents in Industry 4.0;
  • Design methodology, middlewares, principles, infrastructures, and tools for the IIoT;
  • Application of CPS in smart domains (e.g., manufacturing, agriculture, building);
  • Industrial applications of multi-agent systems;
  • Industrial agents in industry.

Prof. Dr. José Barbosa
Dr. Luis Ribeiro
Prof. Dr. Paulo Leitao
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

  • digitalization
  • industry 4.0
  • internet of things
  • cyberphysical systems
  • multi-agent systems
  • digital twins

Published Papers (6 papers)

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Research

14 pages, 4750 KiB  
Article
Container Terminal Digital Twin Yard System Construction
by Xueqiang Du, Chengji Liang, Ning Zhao and Beng Xuan
Processes 2023, 11(7), 2223; https://doi.org/10.3390/pr11072223 - 24 Jul 2023
Viewed by 1446
Abstract
New requirements for terminal production and operation have emerged as a result of the increase in container terminal throughput. Traditional terminals’ manufacturing capabilities fall short of the expanding service needs. By constructing a digital twin yard for container terminals, the production capacity of [...] Read more.
New requirements for terminal production and operation have emerged as a result of the increase in container terminal throughput. Traditional terminals’ manufacturing capabilities fall short of the expanding service needs. By constructing a digital twin yard for container terminals, the production capacity of terminals can be effectively improved, and the production operation process can be optimized. This paper firstly constructs a digital twin yard system for container terminals, proposing that it is mainly composed of physical space, virtual space, data, services, and intelligent agents. This paper elaborates on the core technologies of digital twin yards and finally takes the container delivery and loading process as an example to solve the production bottlenecks of the yard in the container delivery business by reorganizing the operation process and targeting it, which can improve the terminal production efficiency to a certain extent. Full article
(This article belongs to the Special Issue Digitalized Industrial Production Systems and Industry 4.0, Volume II)
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14 pages, 3748 KiB  
Article
A Software Toolbox for Realistic Dataset Generation for Testing Online and Offline 3D Bin Packing Algorithms
by Luis Ribeiro and Anan Ashrabi Ananno
Processes 2023, 11(7), 1909; https://doi.org/10.3390/pr11071909 - 25 Jun 2023
Cited by 2 | Viewed by 1047
Abstract
Packing products into a pallet or other medium is an unavoidable activity for producing companies. In many cases, packing is based on operator experience and training using packing patterns that have worked before. Automated packing, on the other hand, requires a systematic procedure [...] Read more.
Packing products into a pallet or other medium is an unavoidable activity for producing companies. In many cases, packing is based on operator experience and training using packing patterns that have worked before. Automated packing, on the other hand, requires a systematic procedure for devising packing solutions. In the scientific literature, this problem is known as 3D bin packing (3DBP) and many authors have proposed exact and heuristic solutions for many variations of the problem. There is, however, a lack of datasets that can be used to test and validate such solutions. Many of the available datasets use randomly generated products with extremely limited connection to real practice. Furthermore, they contain a reduced number of product configurations and ignore that packing relates to customers’ orders, which have specific relative mixes of products. This paper proposes a software toolbox for generating arbitrarily large datasets for 3DBPP based on real industry data. The toolbox was developed in connection with the analysis of a real dataset from the food and beverages sector, which enabled the creation of several synthetic datasets. The toolbox and the synthetic datasets are publicly available and can be used to generate additional data for testing and validating 3DBP solutions. The industry is increasingly becoming data dependent and driven. The ability to generate good quality synthetic data to support the development of solutions to real industry problems is of extreme importance. This work is a step in that direction in a domain where open data are scarce. Full article
(This article belongs to the Special Issue Digitalized Industrial Production Systems and Industry 4.0, Volume II)
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19 pages, 2823 KiB  
Article
An Integrated Fuzzy DEMATEL and Fuzzy TOPSIS Method for Analyzing Smart Manufacturing Technologies
by Fawaz M. Abdullah, Abdulrahman M. Al-Ahmari and Saqib Anwar
Processes 2023, 11(3), 906; https://doi.org/10.3390/pr11030906 - 16 Mar 2023
Cited by 10 | Viewed by 2108
Abstract
I4.0 promotes a future in which highly individualized goods are mass produced at a competitive price through autonomous, responsive manufacturing. In order to attain market competitiveness, organizations require proper integration of I4.0 technologies and manufacturing strategy outputs (MSOs). Implementing such a comprehensive integration [...] Read more.
I4.0 promotes a future in which highly individualized goods are mass produced at a competitive price through autonomous, responsive manufacturing. In order to attain market competitiveness, organizations require proper integration of I4.0 technologies and manufacturing strategy outputs (MSOs). Implementing such a comprehensive integration relies on carefully selecting I4.0 technologies to meet industrial requirements. There is little clarity on the impact of I4.0 technologies on MSOs, and the literature provides little attention to this topic. This research investigates the influence of I4.0 technologies on MSOs by combining reliable MCDM methods. This research uses a combination of fuzzy DEMATEL and fuzzy TOPSIS to evaluate the impact of I4.0 technologies on MSOs. The fuzzy theory is implemented in DEMATEL and TOPSIS to deal with the uncertainty and vagueness of human judgment. The FDEMATEL was utilized to identify interrelationships and determine criterion a’s weights, while the fuzzy TOPSIS approach was employed to rank the I4.0 technologies. According to the study’s findings, cost is the most critical factor determining MSOs’ market competitiveness, followed by flexibility and performance. On the other hand, additive manufacturing (AM) is the best I4.0 technology for competing in the global market. The results present an evaluation model for analyzing the relative important weight of multiple factors on MSOs. They can also assist managers in concentrating on the most influential factors and selecting the proper I4.0 Technology to preserve competitiveness. Full article
(This article belongs to the Special Issue Digitalized Industrial Production Systems and Industry 4.0, Volume II)
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25 pages, 12751 KiB  
Article
Cloud-Based Machine Learning Application for Predicting Energy Consumption in Automotive Spot Welding
by Nelson Freitas, Sara Oleiro Araújo, Duarte Alemão, João Ramos, Magno Guedes, José Gonçalves, Ricardo Silva Peres, Andre Dionisio Rocha and José Barata
Processes 2023, 11(1), 284; https://doi.org/10.3390/pr11010284 - 16 Jan 2023
Viewed by 3244
Abstract
The energy consumption of production processes is increasingly becoming a concern for the industry, driven by the high cost of electricity, the growing concern for the environment and the greenhouse emissions. It is necessary to develop and improve energy efficiency systems, to reduce [...] Read more.
The energy consumption of production processes is increasingly becoming a concern for the industry, driven by the high cost of electricity, the growing concern for the environment and the greenhouse emissions. It is necessary to develop and improve energy efficiency systems, to reduce the ecological footprint and production costs. Thus, in this work, a system is developed capable of extracting and evaluating useful data regarding production metrics and outputs. With the extracted data, machine learning-based models were created to predict the expected energy consumption of an automotive spot welding, proving a clear insight into how the input values can contribute to the energy consumption of each product or machine, but also correlate the real values to the ideal ones and use this information to determine if some process is not working as intended. The method is demonstrated in real-world scenarios with robotic cells that meet Volkswagen and Ford standards. The results are promising, as models can accurately predict the expected consumption from the cells and allow managers to infer problems or optimize schedule decisions based on the energy consumption. Additionally, by the nature of the conceived architecture, there is room to expand and build additional systems upon the currently existing software. Full article
(This article belongs to the Special Issue Digitalized Industrial Production Systems and Industry 4.0, Volume II)
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24 pages, 2394 KiB  
Article
Enhance the Injection Molding Quality Prediction with Artificial Intelligence to Reach Zero-Defect Manufacturing
by Bruno Silva, Ruben Marques, Dinis Faustino, Paulo Ilheu, Tiago Santos, João Sousa and André Dionisio Rocha
Processes 2023, 11(1), 62; https://doi.org/10.3390/pr11010062 - 27 Dec 2022
Cited by 3 | Viewed by 7531
Abstract
With the spread of the Industry 4.0 concept, implementing Artificial Intelligence approaches on the shop floor that allow companies to increase their competitiveness in the market is starting to be prioritized. Due to the complexity of the processes used in the industry, the [...] Read more.
With the spread of the Industry 4.0 concept, implementing Artificial Intelligence approaches on the shop floor that allow companies to increase their competitiveness in the market is starting to be prioritized. Due to the complexity of the processes used in the industry, the inclusion of a real-time Quality Prediction methodology avoids a considerable number of costs to companies. This paper exposes the whole process of introducing Artificial Intelligence in plastic injection molding processes in a company in Portugal. All the implementations and methodologies used are presented, from data collection to real-time classification, such as Data Augmentation and Human-in-the-Loop labeling, among others. This approach also allows predicting and alerting with regard to process quality loss. This leads to a reduction in the production of non-compliant parts, which increases productivity and reduces costs and environmental footprint. In order to understand the applicability of this system, it was tested in different injection molding processes (traditional and stretch and blow) and with different materials and products. The results of this document show that, with the approach developed and presented, it was possible to achieve an increase in Overall Equipment Effectiveness (OEE) of up to 12%, a reduction in the process downtime of up to 9% and a significant reduction in the number of non-conforming parts produced. This improvement in key performance indicators proves the potential of this solution. Full article
(This article belongs to the Special Issue Digitalized Industrial Production Systems and Industry 4.0, Volume II)
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31 pages, 15530 KiB  
Article
User-Driven: A Product Innovation Design Method for a Digital Twin Combined with Flow Function Analysis
by Min Fu, Yilin Hao, Zefei Gao, Xiaoqing Chen and Xiaoyi Liu
Processes 2022, 10(11), 2353; https://doi.org/10.3390/pr10112353 - 10 Nov 2022
Cited by 3 | Viewed by 1308
Abstract
Since the lack of a specific design method, guidance and user participation in the product innovation design of digital twins, a product innovation design process of a user requirement-driven digital twin combined with flow function analysis is proposed based on the constructed innovation [...] Read more.
Since the lack of a specific design method, guidance and user participation in the product innovation design of digital twins, a product innovation design process of a user requirement-driven digital twin combined with flow function analysis is proposed based on the constructed innovation design model of the PPE-PVE-VVE-VPE digital twin. First, to obtain the orientation of the product innovation design, the user requirement knowledge graph is generated on the basis of product functional decomposition to intuitively express the mapping relationship between user requirements and product functional components. Then, composition analysis of the prototype physical entity (PPE) is conducted in the physical domain; flow function analysis identifies the prototype virtual entity (PVE) defects in the virtual domain; the vision virtual entity (VVE) is solved via flow evolution path as well as evaluated and selected from the users’ perspective to display simulation and rehearsal analysis. Finally, the vision physical entity (VPE) is constructed through the interaction and mapping of the VVE in the physical world, and users are involved in the operation of the VPE. The feasibility and effectiveness of the proposed method are verified by rede-signing a no-tillage maize seeding monomer. Full article
(This article belongs to the Special Issue Digitalized Industrial Production Systems and Industry 4.0, Volume II)
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