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Artificial Intelligence and Machine Learning Applications in Industrial Systems
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Artificial Intelligence and Machine Learning Applications in Industrial Systems

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

Deadline for manuscript submissions: 20 July 2025 | Viewed by 6129

Special Issue Editors

Dr. Fatemeh Davoudi Kakhki

E-Mail Website
Guest Editor
Department of General Engineering, Santa Clara University, Santa Clara, CA 95053, USA
Interests: safety analytics; prognostics health management in industrial systems; applied machine learning; occupational ergonomics
Special Issues, Collections and Topics in MDPI journals
Dr. Maria Kyrarini

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Santa Clara University, 500 El Camino Real, Santa Clara, CA 95053, USA
Interests: assistive robotics; human-robot interaction; human-robot collaboration; robot learning
Special Issues, Collections and Topics in MDPI journals
Dr. Beata Mrugalska

E-Mail Website
Guest Editor
Faculty of Engineering Management, Poznan University of Technology, Poznań, Poland
Interests: lean manufacturing; human factors; Industry 4.0; process control; remaining useful life; robust design and control
Special Issues, Collections and Topics in MDPI journals
Dr. Steven A Freeman

E-Mail Website
Guest Editor
Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011, USA
Interests: occupational safety; agricultural safety; safety education; scholarship of teaching and learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the applications of Machine Learning (ML) and Artificial Intelligence (AI) within industrial systems, aiming to redefine operational efficiency, safety, and productivity. It provides a comprehensive platform to share research, discussions, and advancements in ML and AI engineering applications in enhancing the capabilities of industrial systems. We address applications in smart manufacturing, human–robot collaboration, quality engineering, safety analytics, and risk assessment. Our objective is to explore how these technologies can be applied to create intelligent systems that augment human capabilities, optimize production processes, improve worker safety, and predict system vulnerabilities, thus promoting smarter and safer industrial operations and environment.

We welcome submissions from researchers, academics, and industry practitioners, including original research, reviews, and case studies that explore the integration, challenges, and prospective developments of ML and AI in industrial settings. We look forward to your contributions and to advancing our collective understanding of how ML and AI can shape the future of industrial operations

Dr. Fatemeh Davoudi Kakhki
Dr. Maria Kyrarini
Dr. Beata Mrugalska
Dr. Steven A Freeman
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

  • artificial intelligence
  • machine learning
  • industrial systems
  • operational efficiency
  • safety analytics
  • Human-Robot Interaction
  • quality engineering
  • lean manufacturing

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Published Papers (5 papers)

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Research

24 pages, 1342 KiB  
Article
An Analytical Benchmark of Feature Selection Techniques for Industrial Fault Classification Leveraging Time-Domain Features
by Meltem Süpürtülü, Ayşenur Hatipoğlu and Ersen Yılmaz
Appl. Sci. 2025, 15(3), 1457; https://doi.org/10.3390/app15031457 - 31 Jan 2025
Viewed by 284
Abstract
The growing size and complexity of industrial datasets have intensified the need for efficient fault diagnostics tools. This study addresses the challenge of handling large-scale data by developing a data-driven architecture for fault classification in industrial systems. To extract meaningful insights, 15 time-domain [...] Read more.
The growing size and complexity of industrial datasets have intensified the need for efficient fault diagnostics tools. This study addresses the challenge of handling large-scale data by developing a data-driven architecture for fault classification in industrial systems. To extract meaningful insights, 15 time-domain features were combined with 5 Feature Selection Methods to optimize model performance by eliminating redundant features. The sensor data and selected features were analyzed using the Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) algorithms to enable accurate fault detection and prediction. The proposed framework was validated using publicly available datasets, namely the Case Western Reserve University (CWRU) bearing dataset and the National Aeronautics and Space Administration Ames Prognostics Center of Excellence (NASA PCoE) lithium-ion battery dataset. The results demonstrate the framework’s adaptability and high efficacy across diverse scenarios, achieving an average F1-score exceeding 98.40% using only 10 selected features. This finding highlights the effectiveness of embedded Feature Selection Methods in improving classification performance while reducing computational complexity. The study underscores the potential of the proposed framework as a foundational tool in intelligent manufacturing, offering a versatile solution to enhance fault diagnostics in diverse industrial applications. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Industrial Systems)
22 pages, 11326 KiB  
Article
Optimizing Bioleaching for Printed Circuit Board Copper Recovery: An AI-Driven RGB-Based Approach
by Jordi Vives Pons, Albert Comerma, Teresa Escobet, Antonio D. Dorado and Marta I. Tarrés-Puertas
Appl. Sci. 2025, 15(1), 129; https://doi.org/10.3390/app15010129 - 27 Dec 2024
Viewed by 672
Abstract
Recovering copper from end-of-life electronics, especially from printed circuit boards, provides significant economic benefits, reduces environmental impact, and supports a circular economy. This case study presents a data-driven approach to predicting copper recovery in the electrolysis stage of a bioleaching process by utilizing [...] Read more.
Recovering copper from end-of-life electronics, especially from printed circuit boards, provides significant economic benefits, reduces environmental impact, and supports a circular economy. This case study presents a data-driven approach to predicting copper recovery in the electrolysis stage of a bioleaching process by utilizing RGB sensor readings. We tested nine regression models using RGB values from experimental data. The gradient boosting model, optimized via response surface methodology (RSM), outperformed the others, with predictions matching 84% of observed patterns. These results demonstrate strong predictive capabilities, with scope for further accuracy enhancements. We offer an open-source, web-based digital twin designed specifically to monitor the bioleaching plant, enabling real-time and historical data analysis to support predictive maintenance. Our results underscore the potential to optimize the entire bioleaching process, marking a significant advancement for large-scale copper recovery. This study is the first to investigate predictive bioleaching continuous processes in a semi-industrial e-waste plant using RGB sensors, presenting a novel approach in the field. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Industrial Systems)
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15 pages, 882 KiB  
Article
Dynamic Pricing Method in the E-Commerce Industry Using Machine Learning
by Marcin Nowak and Marta Pawłowska-Nowak
Appl. Sci. 2024, 14(24), 11668; https://doi.org/10.3390/app142411668 - 13 Dec 2024
Viewed by 1414
Abstract
One of the key areas of contemporary marketing is the formulation of a pricing strategy, which is one of the four pillars of the traditional marketing mix. One way to implement this strategy is through dynamic pricing. It is currently gaining popularity in [...] Read more.
One of the key areas of contemporary marketing is the formulation of a pricing strategy, which is one of the four pillars of the traditional marketing mix. One way to implement this strategy is through dynamic pricing. It is currently gaining popularity in many industries for two reasons. Firstly, it is possible, easy, and cheap to collect information about transactions and customers. Secondly, machine learning mechanisms, for which these data are essential, are becoming widely available. The aim of this article is to propose a dynamic pricing method for the e-commerce industry. To achieve this goal, machine learning methods such as the Naive Bayes classifier, support vector machines (linear and nonlinear), decision trees, and the k-nearest neighbor algorithm were used. The empirical results indicate that the linear support vector machine achieved the highest accuracy (86.92%), demonstrating the model’s effectiveness in classifying pricing decisions. This article aligns with two leading research trends in dynamic pricing: personalized dynamic pricing (the target model considers customer-related criteria) and the development of systems to assist managers in optimizing pricing strategies to increase revenues (using machine learning methods). This article presents a literature review on dynamic pricing and then discusses the machine learning methods applied. In the final part of this article, verification of the developed dynamic pricing method using real-world conditions is presented. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Industrial Systems)
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23 pages, 1325 KiB  
Article
Sequential Memetic Algorithm Optimization for Allocation Planning in Hostelry Establishments
by Rubén Ferrero-Guillén, Alberto Martínez-Gutiérrez, Rubén Álvarez and Javier Díez-González
Appl. Sci. 2024, 14(21), 9698; https://doi.org/10.3390/app14219698 - 23 Oct 2024
Viewed by 954
Abstract
Hostelry establishments face the challenge of devising a table and chair allocation for accommodating their customers on a daily basis. This problem scales significantly with the introduction of constraints, such as scenario obstacles or the requirement of a minimum distance separation. The TLP [...] Read more.
Hostelry establishments face the challenge of devising a table and chair allocation for accommodating their customers on a daily basis. This problem scales significantly with the introduction of constraints, such as scenario obstacles or the requirement of a minimum distance separation. The TLP (Table Location Problem) and the CLP (Chair Location Problem) are NP-Hard complexity problems that aim to attain the optimal table and chair distribution for certain applications. Existing approaches to this problem fail to address both the TLP and CLP simultaneously, thus resulting in suboptimal solutions achieved by imposing optimization constraints. Therefore, in this paper, a sequential optimization methodology based on a GBLS MA (Gradient-Based Local Search Memetic Algorithm) optimizations is proposed for optimizing the table and chair disposition simultaneously while also considering scenario and distancing restrictions. The proposed methodology is then implemented into a realistic establishment, where different optimization strategies within the CLP are compared. Results prove the viability and flexibility of the proposed sequential optimization for complex hostelry scenarios. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Industrial Systems)
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21 pages, 1823 KiB  
Article
Recognition of Intergranular Corrosion in AISI 304 Stainless Steel by Integrating a Multilayer Perceptron Artificial Neural Network and Metallographic Image Processing
by Edgar Augusto Ruelas-Santoyo, Armando Javier Ríos-Lira, Yaquelin Verenice Pantoja-Pacheco, José Alfredo Jiménez-García, Salvador Hernández-González and Oscar Cruz-Domínguez
Appl. Sci. 2024, 14(12), 5077; https://doi.org/10.3390/app14125077 - 11 Jun 2024
Viewed by 1151
Abstract
The correct management of operations in thermoelectric plants is based on the continuous evaluation of the structural integrity of its components, among which there are elements made of stainless steel that perform water conduction functions at elevated temperatures. The working conditions generate progressive [...] Read more.
The correct management of operations in thermoelectric plants is based on the continuous evaluation of the structural integrity of its components, among which there are elements made of stainless steel that perform water conduction functions at elevated temperatures. The working conditions generate progressive wear that must be monitored from the perspective of the microstructure of the material. When AISI 304 stainless steel is subjected to a temperature range between 450 and 850 °C, it is susceptible to intergranular corrosion. This phenomenon, known as sensitization, causes the material to lose strength and generates different patterns in its microstructure. This research analyzes three different patterns present in the microstructure of stainless steel, which manifest themselves through the following characteristics: the absence of intergranular corrosion, the presence of intergranular corrosion, and the precipitation of chromium carbides. This article shows the development of a methodology capable of recognizing the corrosion patterns generated in stainless steel with an accuracy of 98%, through the integration of a multilayer perceptron neural network and the following digital image processing methods: phase congruence and a gray-level co-occurrence matrix. In this way, an automatic procedure for the analysis of the intergranular corrosion present in AISI 304 stainless steel using artificial intelligence is proposed. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applications in Industrial Systems)
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