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Applied Sciences
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Digital Technologies Enabling Modern Industries, 2nd Edition
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Digital Technologies Enabling Modern Industries, 2nd Edition

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

Deadline for manuscript submissions: 30 November 2026 | Viewed by 2174

Special Issue Editors

Dr. Janis Arents

E-Mail Website
Guest Editor
Institute of Electronics and Computer Science, LV-1006 Riga, Latvia
Interests: robotics; mobile manipulators; grasping; AI-based systems; perception
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vytautas Bucinskas

E-Mail Website
Guest Editor
Department of Mechatronics, Robotics, and Digital Manufacturing, Vilnius Gediminas Technical University, LT-10105 Vilnius, Lithuania
Interests: biodegradable material; fexible strain sensors; robotic process automation; artificial intelligence in robotics; virtual and augmented reality in industry; smart sensors and systems
Special Issues, Collections and Topics in MDPI journals
Dr. Andrius Dzedzickis

E-Mail Website
Guest Editor
Department of Mechatronics, Robotics, and Digital Manufacturing, Vilnius Gediminas Technical University, LT-10105 Vilnius, Lithuania
Interests: robotic process automation; artificial intelligence in robotics; virtual and augmented reality in industry; digital twins of industrial systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital technologies are becoming the main factor fostering the development of modern industries and have come to affect all industrial sectors and all aspects thereof, from orders and resource management to final product delivery and maintenance.

In this upcoming Special Issue, entitled "Digital Technologies Enabling Modern Industries, 2nd Edition", we aim to highlight the transformative power digital technologies have to reshape modern industries. This Special Issue is dedicated to uncovering how advancements in robotics, artificial intelligence, the Internet of things (IoT), and other digital innovations are synergizing to redefine traditional practices, enhance productivity, and facilitate sustainable growth. The focus ranges from the deployment of robotic solutions in complex environments to the seamless integration of AI for smarter decision-making and operational efficiency. Robotics, central to this transformation, are evolving beyond their conventional roles, driven by breakthroughs in perception, navigation, grasping techniques, natural language processing, and many other aspects. These technologies are enabling autonomous operations in diverse environments and their power to enhance synergy with human workers is constantly evolving. Simultaneously, sensors and the IoT, technologies that stand at the forefront of the digital revolution, are another pivotal aspect covered by this Special Issue. These technologies are instrumental to creating interconnected ecosystems within industries, enabling the seamless collection, transmission, and analysis of data.

Dr. Janis Arents
Prof. Dr. Vytautas Bucinskas
Dr. Andrius Dzedzickis
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 250 words) can be sent to the Editorial Office for assessment.

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

  • perception
  • navigation
  • robotics
  • automation
  • robotic grasping
  • sensors
  • IoT
  • simulation
  • synthetic data
  • multiagent systems
  • smart manufacturing
  • artificial intelligence
  • virtual and augmented reality
  • digital twins

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Related Special Issue

Published Papers (3 papers)

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Research

36 pages, 11621 KB  
Article
Predictive Modelling of Nitrogen Content in Molten Metal During BOF Steelmaking Processes via Python-Based Machine Learning: A Benchmarking of Statistical Techniques
by Jaroslav Demeter, Branislav Buľko and Martina Hrubovčáková
Appl. Sci. 2026, 16(8), 3774; https://doi.org/10.3390/app16083774 - 12 Apr 2026
Viewed by 476
Abstract
This study benchmarks eight Python-based machine learning models for predicting nitrogen content across four sequential stages of BOF steelmaking. A dataset of 291 metallic samples from 76 heats was employed, covering pig iron desulfurization (PHASE #1), crude steel before BOF tapping (PHASE #2), [...] Read more.
This study benchmarks eight Python-based machine learning models for predicting nitrogen content across four sequential stages of BOF steelmaking. A dataset of 291 metallic samples from 76 heats was employed, covering pig iron desulfurization (PHASE #1), crude steel before BOF tapping (PHASE #2), and secondary metallurgy start (PHASE #3) and completion (PHASE #4). Linear regression, polynomial regression, ridge regression, decision tree, random forest, feedforward neural networks (FNNs), Gaussian Process Regression (GPR), and Support Vector Regression (SVR) were implemented in Python 3 with Z-score normalization and an 80/20 train–test split, and evaluated via MAE, MSE, MAPE, and R2. Ridge regression achieved the highest accuracy in PHASE #1 (84.59%) and PHASE #4 (84.04%); FNNs excelled in PHASE #2 (78.27%) with consistent cross-phase performance; linear regression was optimal for PHASE #3 (79.06%). The advanced kernel-based methods demonstrated competitive performance, with GPR achieving 84.73% in PHASE #1 and SVR attaining 77.10% in PHASE #3 and 83.40% in PHASE #4, confirming their suitability for limited industrial datasets with a nonlinear structure. A hybrid strategy remains recommended: ridge regression for PHASES #1 and #4, FNNs for PHASES #2 and #4, and linear regression for PHASE #3, with SVR as a robust alternative in phases with moderate nonlinearity. Full article
(This article belongs to the Special Issue Digital Technologies Enabling Modern Industries, 2nd Edition)
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18 pages, 5475 KB  
Article
Small PCB Defect Detection Based on Convolutional Block Attention Mechanism and YOLOv8
by Zhe Sun, Ruihan Ma and Qujiang Lei
Appl. Sci. 2026, 16(2), 1078; https://doi.org/10.3390/app16021078 - 21 Jan 2026
Viewed by 714
Abstract
Automated defect detection in printed circuit boards (PCBs) is a critical process for ensuring the quality and reliability of electronic products. To address the limitations of existing detection methods, such as insufficient sensitivity to minor defects and limited recognition accuracy in complex backgrounds, [...] Read more.
Automated defect detection in printed circuit boards (PCBs) is a critical process for ensuring the quality and reliability of electronic products. To address the limitations of existing detection methods, such as insufficient sensitivity to minor defects and limited recognition accuracy in complex backgrounds, this paper proposes an enhanced YOLOv8 detection framework. The core contribution lies not merely in the integration of the Convolutional Block Attention Module (CBAM), but in a principled and task-specific integration strategy designed to address the multi-scale and low-contrast nature of PCB defects. The complete CBAM is integrated into the multi-scale feature layers (P3, P4, P5) of the YOLOv8 backbone network. By leveraging sequential channel and spatial attention submodules, CBAM guides the model to dynamically optimise feature responses, thereby significantly enhancing feature extraction for tiny, morphologically diverse defects. Experiments on a public PCB defect dataset demonstrate that the proposed model achieves a mean average precision (mAP@50) of 98.8% while maintaining real-time inference speed, surpassing the baseline YOLOv8 model by 9.5%, with the improvements of 7.4% in precision and 12.3% in recall. While the model incurs a higher computational cost (79.4 GFLOPs), it maintains a real-time inference speed of 109.11 FPS, offering a viable trade-off between accuracy and efficiency for high-precision industrial inspection. The proposed model demonstrates superior performance in detecting small-scale defects, making it highly suitable for industrial deployment. Full article
(This article belongs to the Special Issue Digital Technologies Enabling Modern Industries, 2nd Edition)
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30 pages, 3448 KB  
Article
Automated Machine Learning for Nitrogen Content Prediction in Steel Production: A Comprehensive Multi-Stage Process Analysis
by Jaroslav Demeter, Branislav Buľko, Peter Demeter, Martina Hrubovčáková, Slavomír Hubatka and Lukáš Fogaraš
Appl. Sci. 2026, 16(1), 441; https://doi.org/10.3390/app16010441 - 31 Dec 2025
Cited by 2 | Viewed by 634
Abstract
Nitrogen control in steel production critically influences mechanical properties and product quality, yet traditional mechanistic models struggle to capture complex multivariable interactions across the complete steelmaking chain. This study developed and validated automated machine learning (AutoML) models using Microsoft Azure Machine Learning Studio [...] Read more.
Nitrogen control in steel production critically influences mechanical properties and product quality, yet traditional mechanistic models struggle to capture complex multivariable interactions across the complete steelmaking chain. This study developed and validated automated machine learning (AutoML) models using Microsoft Azure Machine Learning Studio to predict nitrogen content at four critical stages: desulfurization of pig iron (Stage 1), basic oxygen furnace prior to tapping (Stage 2), secondary steelmaking initiation (Stage 3), and secondary steelmaking finishing (Stage 4). Industrial data from 291 metal samples across 76 heats were collected and processed, with stage-specific models employing stack ensemble architectures combining 4–7 algorithms with feature sets ranging from 12 to 35 variables. The models achieved normalized root mean squared errors between 0.112–0.149, mean absolute percentage errors of 14.6–21.1%, and Spearman correlations of 0.310–0.587, with secondary steelmaking models demonstrating superior performance due to more controlled thermodynamic conditions. All models achieved sub-second prediction latencies suitable for real-time industrial implementation. This research demonstrates that AutoML effectively captures complex physicochemical relationships governing nitrogen behavior throughout the steelmaking process, providing practical solutions for Industry 4.0 applications in steelmaking process control and quality optimization. Full article
(This article belongs to the Special Issue Digital Technologies Enabling Modern Industries, 2nd Edition)
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