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

Deadline for manuscript submissions: 20 April 2025 | Viewed by 21067

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Special Issue Editors

Dr. Ivo Pereira

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Guest Editor

Faculty of Science and Technology, Fernando Pessoa University, 4249-004 Porto, Portugal
Interests: artificial intelligence; machine learning; data science; marketing automation; combinatorial optimization; Industry 4.0
Special Issues, Collections and Topics in MDPI journals

Dr. Ana Madureira

E-Mail Website
Guest Editor

Institute of Engineering, Polytechnic of Porto, 4200-072 Porto, Portugal
Interests: data science; bigdata, edge computing, Internet of Things; Industry 4.0 and digitalization; deep & machine learning; machine learning and artificial intelligence

Dr. Benjamim Fonseca

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Guest Editor

INESC TEC, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
Interests: collaborative learning; computational thinking; computer supported cooperative work; human-computer interaction; optimization; reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Fourth Industrial Revolution (Industry 4.0) highlights the implementation of automation and digitization across industry domains. The Fifth Industrial Revolution (Industry 5.0) will maximize human and technological strengths, making human–machine collaboration more harmonious.

This Special Issue aims to collect high-quality scientific papers of recent advances and applications related to automation and digitization in industry domains. Papers on not only Industry 4.0 but also Industry 5.0 are welcome. We encourage researchers from different fields within the journal’s scope to contribute with the latest developments in their research field and/or to invite relevant experts and colleagues to do so.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Artificial intelligence and machine learning;
Internet of Things and Sensors;
Edge computing and cloud services;
Control and systems engineering;
Modeling and simulation;
Human-centered automation;
Process automation and control;
Robotics and machine automation;
Digital twins;
Cyberphysical systems;
Process simulation and automation;
Supply chain management;
Predictive maintenance.

We look forward to receiving your contributions.

Dr. Ivo Pereira
Dr. Ana Madureira
Dr. Benjamim Fonseca
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

automation
digitization
industry
advances and applications
digital transformation
Internet of Things
artificial intelligence
machine learning
interoperability
multidisciplinarity and interdisciplinarity

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Published Papers (12 papers)

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Research

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15 pages, 3280 KiB

Open AccessArticle

Spatial and Temporal Analysis of Surface Displacements for Tailings Storage Facility Stability Assessment

by Wioletta Koperska, Paweł Stefaniak, Maria Stachowiak, Sergii Anufriiev, Ioannis Kakogiannos and Francisco Hernández-Ramírez

Appl. Sci. 2024, 14(22), 10715; https://doi.org/10.3390/app142210715 - 19 Nov 2024

Viewed by 412

Abstract

Monitoring the stability of tailings storage facilities (TSFs) is extremely important due to the catastrophic consequences of instability, which pose a threat to both the environment and human life. For this reason, numerous laboratory and field tests are carried out around dams. An extensive database is collected as part of monitoring and field research. The in-depth analysis of available data can help monitor stability and predict disaster hazards. One of the important factors is displacement, including surface displacements—recorded by benchmarks as well as underground displacements—recorded by inclinometers. In this work, methods were developed to help assess the stability of the TSF in terms of surface and underground displacement based on the simulated data from geodetic benchmarks. The context of spatial correlation was investigated using hot spot analysis, which shows areas of greater risk, indicating the places of correlation of large and small displacements. The analysis of displacements versus time allowed us to indicate the growing exponential trend, thanks to which it is possible to forecast the trend of future displacements, as well as their velocity and acceleration, with the coefficient of determination of the trend matching reaching even 0.97. Additionally, the use of a geographically weighted regression model was proposed to predict the risk of shear relative to surface displacements. Full article

(This article belongs to the Special Issue Automation and Digitization in Industry: Advances and Applications)

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22 pages, 5345 KiB

Open AccessArticle

Detection of Defects in Polyethylene and Polyamide Flat Panels Using Airborne Ultrasound-Traditional and Machine Learning Approach

by Artur Krolik, Radosław Drelich, Michał Pakuła, Dariusz Mikołajewski and Izabela Rojek

Appl. Sci. 2024, 14(22), 10638; https://doi.org/10.3390/app142210638 - 18 Nov 2024

Viewed by 403

Abstract

This paper presents the use of noncontact ultrasound for the nondestructive detection of defects in two plastic plates made of polyamide (PA6) and polyethylene (PE). The aim of the study was to: (1) assess the presence of defects as well as their size, type, and orientation based on the amplitudes of Lamb ultrasonic waves measured in plates made of polyamide (PA6) and polyethylene (PE) due to their homogeneous internal structure, which mainly determined the selection of such model materials for testing; and (2) verify the possibilities of building automatic quality control and defect detection systems based on ML based on the results of the above-mentioned studies within the Industry 4.0/5.0 paradigm. Tests were conducted on plates with generated synthetic defects resembling defects found in real materials such as delamination and cracking at the edge of the plate and a crack (discontinuity) in the center of the plate. Defect sizes ranged from 1 mm to 15 mm. Probes at 30 kHz were used to excite Lamb waves in the slab material. This method is sensitive to the slightest changes in material integrity. A significant decrease in signal amplitude was observed, even for defects of a few millimeters in length. In addition to traditional methods, machine learning (ML) was used for the analysis, allowing an initial assessment of the method’s potential for building cyber-physical systems and digital twins. By training ML models on ultrasonic data, algorithms can distinguish subtle differences between signals reflected from normal and defective areas of the material. Defect types such as voids, cracks, or weak bonds often produce distinct acoustic signatures, which ML models can learn to recognize with high accuracy. Using techniques like feature extraction, ML can process these high-dimensional ultrasonic datasets, identifying patterns that human inspectors might overlook. Furthermore, ML models are adaptable, allowing the same trained algorithms to work on various material batches or panel types with minimal retraining. This combination of automation and precision significantly enhances the reliability and efficiency of quality control in industrial manufacturing settings. The achieved accuracy results, 0.9431 in classification and 0.9721 in prediction, are comparable to or better than the AI-based quality control results in other noninvasive methods of flat surface defect detection, and in the presented ultrasonic method, they are the first described in this way. This approach demonstrates the novelty and contribution of artificial intelligence (AI) methods and tools, significantly extending and automating existing applications of traditional methods. The susceptibility to augmentation by AI/ML may represent an important new property of traditional methods crucial to assessing their suitability for future Industry 4.0/5.0 applications. Full article

(This article belongs to the Special Issue Automation and Digitization in Industry: Advances and Applications)

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13 pages, 3900 KiB

Open AccessArticle

Application Cluster Analysis as a Support form Modelling and Digitalizing the Logistics Processes in Warehousing

by Jana Kronova, Gabriela Izarikova, Peter Trebuna, Miriam Pekarcikova and Milan Filo

Appl. Sci. 2024, 14(11), 4343; https://doi.org/10.3390/app14114343 - 21 May 2024

Cited by 1 | Viewed by 700

Abstract

The article deals with the application of cluster analysis in modeling in-house processes, specifically supply processes. An algorithm is designed on the theoretical basis of cluster analysis and according to the analysis of the supply processes in selected industrial companies. Specifically, the algorithm is based on the hierarchical methods of cluster analysis, and the selected hierarchical clustering method is applicable in modeling storage systems under various production conditions in industrial companies. The methodology of clustering regarding the supply processes is subsequently experimentally verified. Based on the results of the cluster analysis, a system of organization was proposed for the analyzed warehouse in the form of 2D and 3D layout models of the warehouse. Full article

(This article belongs to the Special Issue Automation and Digitization in Industry: Advances and Applications)

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17 pages, 7126 KiB

Open AccessArticle

Enhancing Vibration Analysis in Hydraulic Presses: A Case Study Evaluation

by Daniel Jancarczyk, Ireneusz Wróbel, Piotr Danielczyk and Marcin Sidzina

Appl. Sci. 2024, 14(7), 3097; https://doi.org/10.3390/app14073097 - 7 Apr 2024

Viewed by 1805

Abstract

Vibration monitoring is essential for maintaining the optimal performance and reliability of industrial machinery, which experiences dynamic forces and vibrations during operation. This study delved into a comprehensive analysis of vibration monitoring in hydraulic presses, utilizing advanced measurement systems equipped with accelerometers. The proposed system included a three-axis accelerometer, data acquisition unit, and dedicated measurement software, facilitating the precise monitoring and analysis of vibrations. The influence of the sensor placement and sampling frequency on the measurement results was examined. A time and frequency analysis of the recorded measurements was performed. The results demonstrated the correlation between vibration levels and various production parameters, such as the number of parts simultaneously produced and press pressure. These findings underscore the potential for vibration measurement as a pivotal component in controlling production parameter settings. Full article

(This article belongs to the Special Issue Automation and Digitization in Industry: Advances and Applications)

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17 pages, 609 KiB

Open AccessArticle

Towards Hyper-Relevance in Marketing: Development of a Hybrid Cold-Start Recommender System

by Leonor Fernandes, Vera Miguéis, Ivo Pereira and Eduardo e Oliveira

Appl. Sci. 2023, 13(23), 12749; https://doi.org/10.3390/app132312749 - 28 Nov 2023

Viewed by 1347

Abstract

Recommender systems position themselves as powerful tools in the support of relevance and personalization, presenting remarkable potential in the area of marketing. The cold-start customer problematic presents a challenge within this topic, leading to the need of distinguishing user features and preferences based on a restricted set of transactional information. This paper proposes a hybrid recommender system that aims to leverage transactional and portfolio information as indicating characteristics of customer behaviour. Four independent systems are combined through a parallelised weighted hybrid design. The first individual system utilises the price, target age, and brand of each product to develop a content-based recommender system, identifying item similarities. Secondly, a keyword-based content system uses product titles and descriptions to identify related groups of items. The third system utilises transactional data, defining similarity between products based on purchasing patterns, categorised as a collaborative model. The fourth system distinguishes itself from the previous approaches by leveraging association rules, using transactional information to establish antecedent and precedence relationships between items through a market basket analysis. Two datasets were analysed: product portfolio and transactional datasets. The product portfolio had 17,118 unique products and the included 4,408,825 instances from 2 June 2021 until 2 June 2022. Although the collaborative system demonstrated the best evaluation metrics when comparing all systems individually, the hybridisation of the four systems surpassed each of the individual systems in performance, with a 8.9% hit rate, 6.6% portfolio coverage, and with closer targeting of customer preferences and smaller bias. Full article

(This article belongs to the Special Issue Automation and Digitization in Industry: Advances and Applications)

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22 pages, 446 KiB

Open AccessArticle

Evaluating the Effectiveness of Designs for Low-Cost Digital Manufacturing Systems

by Jan Kaiser, Gregory Hawkridge, Anandarup Mukherjee and Duncan McFarlane

Appl. Sci. 2023, 13(23), 12618; https://doi.org/10.3390/app132312618 - 23 Nov 2023

Cited by 2 | Viewed by 1197

Abstract

There are many well-known systematic approaches to design the digital systems used in manufacturing. However, there are only a few approaches that specifically deal with low-cost components. Such components may not provide the same level of completeness as more expensive industrial alternatives and may need to be combined with other components to become comparable. Consequently, common design challenges for systems comprising such low-cost components revolve around extendability and interface standardisation. There is a need for analysing the capability of the existing approaches to design these systems. This study aims to evaluate the effectiveness of designs for low-cost digital manufacturing systems that have been derived from a particular design approach. The proposed evaluation methodology is used for the special case of designs that are directly based on reference architectures and for the development of specific metrics for that purpose. To quantify the effectiveness, these metrics are applied to a number of design scenarios. Although focusing on reference-architecture-based designs, the proposed methodology can also be used for other design approaches. The evaluation and structured implementation comparison helps practitioners in selecting an effective design approach to low-cost digital manufacturing systems and provides insights into how a particular design approach can become more effective. Full article

(This article belongs to the Special Issue Automation and Digitization in Industry: Advances and Applications)

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10 pages, 514 KiB

Open AccessArticle

Perspectives on Digital Transformation Initiatives in the Mechanical Engineering Industry

by Andrej Miklosik and Alexander Bernhard Krah

Appl. Sci. 2023, 13(22), 12386; https://doi.org/10.3390/app132212386 - 16 Nov 2023

Viewed by 1378

Abstract

Companies from the mechanical engineering industry are eager to embrace new technologies in their pursuit of a competitive advantage. However, the complete digitalization of the sector encounters limitations, as certain aspects necessitate human supervision or manual labor. This is where the concepts of Industry 4.0, Industry 5.0, and digital transformation become relevant. The aim of the research presented in this paper was to gather and extract valuable insights and lessons from the experiences of German companies in the plastic extrusion machinery sector with digital transformation (DT). Qualitative interpretative research was used, using in-depth expert interviews with C-level executives. We organized the findings into three categories: (i) DT communication initiatives, including the elimination of paper, CRM solutions, messenger services, home office, and online procurement platforms; (ii) departments and areas most involved, including accounting and procurement, sales and production, and construction; and (iii) cost–benefit perception, including positive assessment, long-term impacts, and variation from company to company. The results provide valuable insights into the progress of DT initiatives in companies operating in the pipe extrusion sector in Germany. Additionally, several DT misconceptions were identified, thereby enriching the DT misconceptions framework that has been intensely discussed in the DT literature. Full article

(This article belongs to the Special Issue Automation and Digitization in Industry: Advances and Applications)

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24 pages, 13418 KiB

Open AccessArticle

DAMP-YOLO: A Lightweight Network Based on Deformable Features and Aggregation for Meter Reading Recognition

by Sichao Zhuo, Xiaoming Zhang, Ziyi Chen, Wei Wei, Fang Wang, Quanlong Li and Yufan Guan

Appl. Sci. 2023, 13(20), 11493; https://doi.org/10.3390/app132011493 - 20 Oct 2023

Cited by 6 | Viewed by 3128

Abstract

With the development of Industry 4.0, although some smart meters have appeared on the market, traditional mechanical meters are still widely used due to their long-standing presence and the difficulty of modifying or replacing them in large quantities. Most meter readings are still manually taken on-site, and some are even taken in high-risk locations such as hazardous chemical storage. However, existing methods often fail to provide real-time detections or result in misreadings due to the complex nature of natural environments. Thus, we propose a lightweight network called DAMP-YOLO. It combines the deformable CSP bottleneck (DCB) module, aggregated triplet attention (ATA) mechanism, meter data augmentation (MDA), and network pruning (NP) with the YOLOv8 model. In the meter reading recognition dataset, the model parameters decreased by 30.64% while mAP50:95 rose from 87.92% to 88.82%, with a short inference time of 129.6 ms for the Jetson TX1 intelligent car. In the VOC dataset, our model demonstrated improved performance, with mAP50:95 increasing from 41.03% to 45.64%. The experimental results show that the proposed model is competitive for general object detection tasks and possesses exceptional feature extraction capabilities. Additionally, we have devised and implemented a pipeline on the Jetson TX1 intelligent vehicle, facilitating real-time meter reading recognition in situations where manual interventions are inconvenient and hazardous, thereby confirming its feasibility for practical applications. Full article

(This article belongs to the Special Issue Automation and Digitization in Industry: Advances and Applications)

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15 pages, 6583 KiB

Open AccessArticle

Application of Digital Engineering Methods in Order to Improve Processes in Heterogeneous Companies

by Jozef Trojan, Peter Trebuňa and Marek Mizerák

Appl. Sci. 2023, 13(13), 7681; https://doi.org/10.3390/app13137681 - 29 Jun 2023

Cited by 4 | Viewed by 1040

Abstract

In the presented article, the initial phase is focused mainly on familiarization with the production focus of individual enterprises and their financial security, which provides a basis for the proposal of possible solutions to shortcomings. A more detailed study of production through simulation studies using Process Simulate software provides a more comprehensive view of production in the assessed enterprises, where the current course of production at workplaces is evaluated in detail, and suggestions are then offered to improve the revealed shortcomings. In the end, there is an assessment of the companies from the point of view of homogeneity, so that it is then possible to make an inter-industry comparison of the revealed errors and shortcomings, with the result of finding common recommendations for a wide industrial spectrum. Full article

(This article belongs to the Special Issue Automation and Digitization in Industry: Advances and Applications)

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32 pages, 1083 KiB

Open AccessArticle

Autonomous Navigation of Robots: Optimization with DQN

by Juan Escobar-Naranjo, Gustavo Caiza, Paulina Ayala, Edisson Jordan, Carlos A. Garcia and Marcelo V. Garcia

Appl. Sci. 2023, 13(12), 7202; https://doi.org/10.3390/app13127202 - 16 Jun 2023

Cited by 12 | Viewed by 5167

Abstract

In the field of artificial intelligence, control systems for mobile robots have undergone significant advancements, particularly within the realm of autonomous learning. However, previous studies have primarily focused on predefined paths, neglecting real-time obstacle avoidance and trajectory reconfiguration. This research introduces a novel algorithm that integrates reinforcement learning with the Deep Q-Network (DQN) to empower an agent with the ability to execute actions, gather information from a simulated environment in Gazebo, and maximize rewards. Through a series of carefully designed experiments, the algorithm’s parameters were meticulously configured, and its performance was rigorously validated. Unlike conventional navigation systems, our approach embraces the exploration of the environment, facilitating effective trajectory planning based on acquired knowledge. By leveraging randomized training conditions within a simulated environment, the DQN network exhibits superior capabilities in computing complex functions compared to traditional methods. This breakthrough underscores the potential of our algorithm to significantly enhance the autonomous learning capacities of mobile robots. Full article

(This article belongs to the Special Issue Automation and Digitization in Industry: Advances and Applications)

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18 pages, 884 KiB

Open AccessArticle

Overlap in Automatic Root Cause Analysis in Manufacturing: An Information Theory-Based Approach

by Eduardo e Oliveira, Vera L. Miguéis and José L. Borges

Appl. Sci. 2023, 13(6), 3416; https://doi.org/10.3390/app13063416 - 8 Mar 2023

Cited by 3 | Viewed by 2006

Abstract

Automatic Root Cause Analysis solutions aid analysts in finding problems’ root causes by using automatic data analysis. When trying to locate the root cause of a problem in a manufacturing process, an issue-denominated overlap can occur. Overlap can impede automated diagnosis using algorithms, as the data make it impossible to discern the influence of each machine on the quality of products. This paper proposes a new measure of overlap based on an information theory concept called Positive Mutual Information. This new measure allows for a more detailed analysis. A new approach is developed for automatically finding the root causes of problems when overlap occurs. A visualization that depicts overlapped locations is also proposed to ease practitioners’ analysis. The proposed solution is validated in simulated and real case-study data. Compared to previous solutions, the proposed approach improves the capacity to pinpoint a problem’s root causes. Full article

(This article belongs to the Special Issue Automation and Digitization in Industry: Advances and Applications)

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Review

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29 pages, 5641 KiB

Open AccessReview

ML-Based Maintenance and Control Process Analysis, Simulation, and Automation—A Review

by Izabela Rojek, Dariusz Mikołajewski, Ewa Dostatni, Adrianna Piszcz and Krzysztof Galas

Appl. Sci. 2024, 14(19), 8774; https://doi.org/10.3390/app14198774 - 28 Sep 2024

Viewed by 1100

Abstract

Automation and digitalization in various industries towards the Industry 4.0/5.0 paradigms are rapidly progressing thanks to the use of sensors, Industrial Internet of Things (IIoT), and advanced fifth generation (5G) and sixth generation (6G) mobile networks supported by simulation and automation of processes using artificial intelligence (AI) and machine learning (ML). Ensuring the continuity of operations under different conditions is becoming a key factor. One of the most frequently requested solutions is currently predictive maintenance, i.e., the simulation and automation of maintenance processes based on ML. This article aims to extract the main trends in the area of ML-based predictive maintenance present in studies and publications, critically evaluate and compare them, and define priorities for their research and development based on our own experience and a literature review. We provide examples of how BCI-controlled predictive maintenance due to brain–computer interfaces (BCIs) play a transformative role in AI-based predictive maintenance, enabling direct human interaction with complex systems. Full article

(This article belongs to the Special Issue Automation and Digitization in Industry: Advances and Applications)

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