Special Issue "Industrial Big Data and Process Modelling for Smart Manufacturing"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: 31 March 2023 | Viewed by 3636

Special Issue Editors

Prof. Stefano Carrino
E-Mail Website
Guest Editor
Data analytics Group, Haute Ecole Arc, University of Applied Sciences and Arts Western Switzerland, Rue de la Serre 7, 2610 St. Imier, Switzerland
Interests: artificial intelligence; machine learning; industry 4.0
Dr. Vicente Rodríguez Montequín
E-Mail Website
Guest Editor
Project Engineering Area, University of Oviedo C/Independencia 13, 33004 Oviedo, Spain
Interests: data science and engineering; smart manufacturing; process modeling and optimization; project management
Prof. Hatem Ghorbel
E-Mail Website
Guest Editor
Data analytics Group, Haute Ecole Arc, University of Applied Sciences and Arts Western Switzerland, Rue de la Serre 7, 2610 St. Imier, Switzerland
Interests: artificial intelligence; machine learning; natural language processing; industry 4.0
Dr. Ivana Budinská
E-Mail Website
Guest Editor
Institute of Informatics, Slovak Academy of Sciences, Dúbravská cesta 9, 845 07 Bratislava 45, Slovakia
Interests: applied informatics; discrete systems modeling and simulation; multi agent systems; artificial intelligence; ontology engineering
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Special Issue Information

Dear Colleagues,

Industry and manufacturing are in the process of unprecedented transformations. The goals of these transformations are to enable production with a higher yield, higher quality, lower costs, lower environmental impact and increased flexibility moving from mass production to mass personalization. The availability of big data and the recent advances in artificial intelligence and process modelling are the keys to these changes. The emergence of techniques such as deep learning and the spreading of technologies such as the Internet of Things has boosted this evolution, actively supported by new achievements in mathematics and artificial intelligence methods focused on the formalisms and algorithm development.

This Special Issue will gather a collection of articles reflecting the latest developments in artificial intelligence and smart manufacturing, including machine (deep) learning, process modelling, big data, soft computing techniques, cyber-physical systems, reinforcement learning, intelligent multi-agent systems, and others.

Contributions are welcome on both theoretical and practical models. The selection criteria consider the formal and technical soundness, experimental support, and the relevance of the contribution.

Prof. Stefano Carrino
Assoc. Prof. Vicente Rodríguez Montequín
Prof. Hatem Ghorbel
Dr. Ivana Budinská
Guest Editors

Manuscript Submission Information

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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. Mathematics 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 1800 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 
  • Process modelling 
  • Process optimization 
  • Computational Intelligence 
  • Artificial intelligence 
  • (Deep) Machine learning 
  • Multi-agent systems 
  • Reinforcement learning
  • Soft Computing 
  • Nature Inspired Computing 
  • Knowledge Base 
  • Ontology 
  • Big data 
  • Cyber-physical systems
  • Internet of Things

Published Papers (4 papers)

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Research

Article
Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)
Mathematics 2022, 10(12), 2031; https://doi.org/10.3390/math10122031 - 11 Jun 2022
Viewed by 349
Abstract
In the current Industry 4.0 revolution, prognostics and health management (PHM) is an emerging field of research. The difficulty of obtaining data from electromechanical systems in an industrial setting increases proportionally with the scale and accessibility of the automated industry, resulting in a [...] Read more.
In the current Industry 4.0 revolution, prognostics and health management (PHM) is an emerging field of research. The difficulty of obtaining data from electromechanical systems in an industrial setting increases proportionally with the scale and accessibility of the automated industry, resulting in a less interpolated PHM system. To put it another way, the development of an accurate PHM system for each industrial system necessitates a unique dataset acquired under specified conditions. In most circumstances, obtaining this one-of-a-kind dataset is difficult, and the resulting dataset has a significant imbalance, a lack of certain useful information, and contains multi-domain knowledge. To address those issues, this paper provides a fault detection and diagnosis system that evaluates and preprocesses imbalanced, scarce, multi-domain (ISMD) data acquired from an industrial robot, utilizing signal processing (SP) techniques and deep learning-based (DL) domain knowledge transfer. The domain knowledge transfer is used to produce a synthetic dataset with a high interpolation rate that contains all the useful information about each domain. For domain knowledge transfer and data generation, continuous wavelet transform (CWT) with a generative adversarial network (GAN) was used, as well as a convolutional neural network (CNN), to test the suggested methodology using transfer learning and categorize several faults. The proposed methodology was tested on a real experimental bench that included an industrial robot created by Hyundai Robotics. This test had a satisfactory outcome with a 99.7% (highest) classification accuracy achieved by transfer learning on several CNN benchmark models. Full article
(This article belongs to the Special Issue Industrial Big Data and Process Modelling for Smart Manufacturing)
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Article
A Sustainable Methodology Using Lean and Smart Manufacturing for the Cleaner Production of Shop Floor Management in Industry 4.0
Mathematics 2022, 10(3), 347; https://doi.org/10.3390/math10030347 - 24 Jan 2022
Cited by 2 | Viewed by 1134
Abstract
The production management system in Industry 4.0 is emphasizes the improvement of productivity within limited constraints by sustainable production planning models. To accomplish this, several approaches are used which include lean manufacturing, kaizen, smart manufacturing, flexible manufacturing systems, cyber–physical systems, artificial intelligence, and [...] Read more.
The production management system in Industry 4.0 is emphasizes the improvement of productivity within limited constraints by sustainable production planning models. To accomplish this, several approaches are used which include lean manufacturing, kaizen, smart manufacturing, flexible manufacturing systems, cyber–physical systems, artificial intelligence, and the industrial Internet of Things in the present scenario. These approaches are used for operations management in industries, and specifically productivity maximization with cleaner shop floor environmental management, and issues such as worker safety and product quality. The present research aimed to develop a methodology for cleaner production management using lean and smart manufacturing in industry 4.0. The developed methodology would able to enhance productivity within restricted resources in the production system. The developed methodology was validated by production enhancement achieved in two case study investigations within the automobile manufacturing industry and a mining machinery assembly unit. The results reveal that the developed methodology could provide a sustainable production system and problem-solving that are key to controlling production shop floor management in the context of industry 4.0. It is also capable of enhancing the productivity level within limited constraints. The novelty of the present research lies in the fact that this type of methodology, which has been developed for the first time, helps the industry individual to enhance production in Industry 4.0 within confined assets by the elimination of several problems encountered in shop floor management. Therefore, the authors of the present study strongly believe that the developed methodology would be beneficial for industry individuals to enhance shop floor management within constraints in industry 4.0. Full article
(This article belongs to the Special Issue Industrial Big Data and Process Modelling for Smart Manufacturing)
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Article
Automatic Path Planning Offloading Mechanism in Edge-Enabled Environments
Mathematics 2021, 9(23), 3117; https://doi.org/10.3390/math9233117 - 03 Dec 2021
Cited by 1 | Viewed by 532
Abstract
The utilization of edge-enabled cloud computing in unmanned aerial vehicles has facilitated advances in autonomous control by employing computationally intensive algorithms frequently related to traversal among different locations in an environment. A significant problem remains in designing an effective strategy to offload tasks [...] Read more.
The utilization of edge-enabled cloud computing in unmanned aerial vehicles has facilitated advances in autonomous control by employing computationally intensive algorithms frequently related to traversal among different locations in an environment. A significant problem remains in designing an effective strategy to offload tasks from the edge to the cloud. This work focuses on creating such a strategy by employing a network evaluation method built on the mean opinion score metrics in concoction with machine learning algorithms for path length prediction to assess computational complexity and classification models to perform an offloading decision on the data provided by both network metrics and solution depth prediction. The proposed system is applied to the A* path planning algorithm, and the presented results demonstrate up to 94% accuracy in offloading decisions. Full article
(This article belongs to the Special Issue Industrial Big Data and Process Modelling for Smart Manufacturing)
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Article
Distributed Mechanism for Detecting Average Consensus with Maximum-Degree Weights in Bipartite Regular Graphs
Mathematics 2021, 9(23), 3020; https://doi.org/10.3390/math9233020 - 25 Nov 2021
Cited by 1 | Viewed by 712
Abstract
In recent decades, distributed consensus-based algorithms for data aggregation have been gaining in importance in wireless sensor networks since their implementation as a complementary mechanism can ensure sensor-measured values with high reliability and optimized energy consumption in spite of imprecise sensor readings. In [...] Read more.
In recent decades, distributed consensus-based algorithms for data aggregation have been gaining in importance in wireless sensor networks since their implementation as a complementary mechanism can ensure sensor-measured values with high reliability and optimized energy consumption in spite of imprecise sensor readings. In the presented article, we address the average consensus algorithm over bipartite regular graphs, where the application of the maximum-degree weights causes the divergence of the algorithm. We provide a spectral analysis of the algorithm, propose a distributed mechanism to detect whether a graph is bipartite regular, and identify how to reconfigure the algorithm so that the convergence of the average consensus algorithm is guaranteed over bipartite regular graphs. More specifically, we identify in the article that only the largest and the smallest eigenvalues of the weight matrix are located on the unit circle; the sum of all the inner states is preserved at each iteration despite the algorithm divergence; and the inner states oscillate between two values close to the arithmetic means determined by the initial inner states from each disjoint subset. The proposed mechanism utilizes the first-order forward and backward finite-difference of the inner states (more specifically, five conditions are proposed) to detect whether a graph is bipartite regular or not. Subsequently, the mixing parameter of the algorithm can be reconfigured the way it is identified in this study whereby the convergence of the algorithm is ensured in bipartite regular graphs. In the experimental part, we tested our mechanism over randomly generated bipartite regular graphs, random graphs, and random geometric graphs with various parameters, thereby identifying its very high detection rate and proving that the algorithm can estimate the arithmetic mean with high precision (like in error-free scenarios) after the suggested reconfiguration. Full article
(This article belongs to the Special Issue Industrial Big Data and Process Modelling for Smart Manufacturing)
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