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Applied Sciences
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Data Mining and Machine Learning in Industrial World
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Data Mining and Machine Learning in Industrial World

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 3968

Special Issue Editors

Dr. Stefan Mocanu

E-Mail Website
Guest Editor
Faculty of Automatic Control and Computer Science, Politehnica University of Bucharest, Bucharest 060042, Romania
Interests: identity management; IoT; WSN; mini-robotics; E-learning
Prof. Dr. Ciprian Dobre

E-Mail Website
Guest Editor
Computer Science Department, Politehnica University of Bucharest, 060042 Bucharest, Romania
Interests: mobile computing; pervasive systems; monitoring tools; context awareness
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A continuous increase in industrial applications and technologies has generated not only variety, but also a high degree of heterogeneity. Items with similar or even identical functionalities and limitations have completely different sizes or shapes, even for the same manufacturer. In the end, all these generate higher costs for the end user, and greater efforts to manage the portfolio for the manufacturer. Automotive industry, for example, was forced to find solutions to these problems by uniting several companies into a single, huge group. In this way, some form of standardization is possible: the same platforms and parts can be used in common by various brands from the group. However, this requires extra effort to find the best ways to find and pair sub-assemblies, and this is where data mining can be extremely useful.  

Manufacturing lines, especially those with the ability to detect defects or wrong parts, can become “smart” using data mining and machine learning techniques. These can also be used for inspection lines. Either way, the results will be a higher level of safety, lower costs, faster, maybe real-time, identification, and removal of problems. This direction is particularly interesting to investigate in the case of older lines that can be modernized.

Industrial fixed robots (robotic arms) are not the only robots that can benefit from data mining and machine learning. Mobile robots are already in use in various warehouses to carry various packages or heavy parts from point A to point B using some form of rigid indoor navigation: color lines, QR codes, BT beacons. Prototypes of partially autonomous robots are tested for home delivery, even if we refer to terrestrial robots or drones. Data mining and machine learning can be of great use for finding optimal routes, detecting and avoiding obstacles or dangers, optimizing multiple deliveries and so on. By doing this, robots’ autonomy will increase and operational costs will be lower.

Regarding autonomy, autonomous driving may be one of the hottest topics of the moment. In fact, we could think of autonomous vehicles as autonomous robots dedicated to carrying people or goods, under severe constraints related to safety, comfort, range, etc. This is another area where data mining could be beneficial. The discussion can be extended to vehicular ad-hoc networks (VANETs), where finding patterns or clustering can help to avoid critical events.

This Issue aims to gather the latest approaches based on data mining in the context of the industrial world. Original research, practical results or emerging ideas are welcomed. The main topics include, but are not limited to:

  • Data Mining in heavy industry.
  • Data Mining in robotic applications.
  • Improving defect detection using Data Mining.
  • Improving autonomous robots’ orientation, service, and autonomy using Data Mining techniques.
  • Data Mining in Autonomous Driving.
  • Data Mining in VANETs.

Dr. Stefan Mocanu
Prof. Dr. Ciprian Dobre
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. 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

  • data mining
  • heavy industry
  • manufacturing lines
  • inspection lines
  • robotic applications
  • decision making
  • autonomous robots
  • autonomous driving

Published Papers (3 papers)

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20 pages, 2969 KiB  
Article
Towards Optimizing Multi-Level Selective Maintenance via Machine Learning Predictive Models
by Amal Achour, Mohamed Ali Kammoun and Zied Hajej
Appl. Sci. 2024, 14(1), 313; https://doi.org/10.3390/app14010313 - 29 Dec 2023
Viewed by 604
Abstract
The maintenance strategies commonly employed in industrial settings primarily rely on theoretical models that often overlook the actual operating conditions. To address this limitation, the present paper introduces a novel selective predictive maintenance approach based on a machine learning model for a multi-parallel [...] Read more.
The maintenance strategies commonly employed in industrial settings primarily rely on theoretical models that often overlook the actual operating conditions. To address this limitation, the present paper introduces a novel selective predictive maintenance approach based on a machine learning model for a multi-parallel series system, which involves executing multiple missions with breaks between them. For this purpose, the proposed selective maintenance approach consists of finding, at each breakdown, the optimal structure of maintenance activities that provide the desired reliability level of the system for each mission. This decision is based on a component’s actual age, as determined by the prediction model. In addition, an optimization model with the Extended Great Deluge (EGD) algorithm uses these predictions as input data to identify the best maintenance level for each component considering the constrained maintenance resources. Finally, the numerical results of the proposed idea applied to the Flexible Manufacturing System (FMS) data are presented to show the robustness of the model. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning in Industrial World)
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23 pages, 2374 KiB  
Article
Generalised Performance Estimation in Novel Hybrid MPC Architectures: Modeling the CONWIP Flow-Shop System
by Silvestro Vespoli, Andrea Grassi, Guido Guizzi and Valentina Popolo
Appl. Sci. 2023, 13(8), 4808; https://doi.org/10.3390/app13084808 - 11 Apr 2023
Viewed by 904
Abstract
The ability to supply increasingly individualized market demand in a short period of time while maintaining costs to a bare minimum might be considered a vital factor for industrialized countries’ competitive revival. Despite significant advances in the field of Industry 4.0, there is [...] Read more.
The ability to supply increasingly individualized market demand in a short period of time while maintaining costs to a bare minimum might be considered a vital factor for industrialized countries’ competitive revival. Despite significant advances in the field of Industry 4.0, there is still an open gap in the literature regarding advanced methodologies for production planning and control. Among different production and control approaches, hybrid architectures are gaining huge interest in the literature. For such architectures to operate at their best, reliable models for performance prediction of the supervised production system are required. In an effort to advance the development of hybrid architecture, this paper develops a model able to predict the performance of the controlled system when it is structured as a controlled work-in-progress (CONWIP) flow-shop with generalized stochastic processing times. To achieve this, we employed a simulation tool using both discrete-event and agent-based simulation techniques, which was then utilized to generate data for training a deep learning neural network. This network was proposed for estimating the throughput of a balanced system, together with a normalization method to generalize the approach. The results showed that the developed estimation tool outperforms the best-known approximated mathematical models while allowing one-shot training of the network. Finally, the paper develops preliminary insights about generalized performance estimation for unbalanced lines. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning in Industrial World)
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18 pages, 4690 KiB  
Article
Effectiveness of Machine-Learning and Deep-Learning Strategies for the Classification of Heat Treatments Applied to Low-Carbon Steels Based on Microstructural Analysis
by Jorge Muñoz-Rodenas, Francisco García-Sevilla, Juana Coello-Sobrino, Alberto Martínez-Martínez and Valentín Miguel-Eguía
Appl. Sci. 2023, 13(6), 3479; https://doi.org/10.3390/app13063479 - 9 Mar 2023
Cited by 2 | Viewed by 1659
Abstract
This work aims to compare the effectiveness of different machine-learning techniques for the image classification of steel microstructures. For this, we use a set of samples of hypoeutectoid steels subjected to three heat treatments: annealing, quenching and quenching with tempering. Logically, the samples [...] Read more.
This work aims to compare the effectiveness of different machine-learning techniques for the image classification of steel microstructures. For this, we use a set of samples of hypoeutectoid steels subjected to three heat treatments: annealing, quenching and quenching with tempering. Logically, the samples contain the typical constituents expected, and these are different for each treatment. Images are obtained by optical microscopy at 400× magnification and from different low-carbon steels to generate the data with some heterogeneity. Learning models are created with an image dataset for classification into three classes based on the respective heat treatments. Likewise, we develop two kinds of models by using, on the one hand, classical machine-learning methods based on the “bag of features” technique and, on the other hand, convolutional neural networks (CNN) with a transfer-learning approach by using GoogLeNet and ResNet50. We demonstrate the superiority of deep-learning techniques (CNN) over classical machine-learning methods. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning in Industrial World)
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