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AI in Industry 4.0
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AI in Industry 4.0

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

Deadline for manuscript submissions: 20 June 2026 | Viewed by 1772

Special Issue Editor

Dr. Ricardo Augusto Rabelo Oliveira

E-Mail Website
Guest Editor
Computer Science Department, Federal University of Ouro Preto, Ouro Preto 35400-000, Brazil
Interests: wearables; Industry 4.0; blockchain

Special Issue Information

Dear Colleagues,

The advance of AI in the context of Industry 4.0 brings new challenges for research. Edge computing is a key aspect for the execution of efficient models in the Industrial plant, considering the recent advances at LLM, Agentic AI and other new tendencies. The challenges of Edge AI involve the integration with 5G and 6G communications, new generations of GenAI, new proposals of federated learning and so on. In this Special Issue we seek to establish a new set of contributions that can achieve the state-of-the-art usage of AI in Industry 4.0.

Dr. Ricardo Augusto Rabelo Oliveira
Guest Editor

Manuscript Submission Information

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Keywords

  • edge computing
  • agentic AI
  • edge AI
  • 5G
  • 6G
  • Industry 4.0
  • federated learning

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Published Papers (3 papers)

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Research

24 pages, 23181 KB  
Article
Kansei Design Optimization of Torque Tool Inspection Cabinets Using XGBoost Prediction Models
by Song Song, Jiaqi Yue and Xihui Yang
Appl. Sci. 2026, 16(8), 3884; https://doi.org/10.3390/app16083884 - 16 Apr 2026
Viewed by 295
Abstract
In the context of the aesthetic economy and the rapid development of digital intelligence, product design is increasingly required to address not only functional performance but also users’ emotional needs. However, due to the ambiguity and subjectivity of perceptual requirements, it remains difficult [...] Read more.
In the context of the aesthetic economy and the rapid development of digital intelligence, product design is increasingly required to address not only functional performance but also users’ emotional needs. However, due to the ambiguity and subjectivity of perceptual requirements, it remains difficult to accurately translate user emotions into specific design solutions. To address this challenge, this study proposes an integrated Kansei Engineering–machine learning framework for optimizing product design. First, user perceptual data are collected through questionnaires and interviews, and key perceptual imagery words are extracted using the Latent Dirichlet Allocation (LDA) model and factor analysis. Then, product design elements are systematically decomposed, and their relative importance is determined using the fuzzy analytic hierarchy process (FAHP). Based on this, a mapping relationship between perceptual imagery and design elements is established. Subsequently, the XGBoost model is employed to predict and optimize design element combinations. The optimized design schemes are further generated using AIGC technology and validated through eye-tracking experiments and subjective evaluations.The results show that the proposed method achieves high predictive accuracy (R2 = 0.87) and significantly improves the emotional expression of product design. This study contributes to the integration of Kansei Engineering and machine learning by providing a data-driven approach for emotional design optimization, offering theoretical, practical, and strategic guidance for intelligent product design in industrial contexts. Full article
(This article belongs to the Special Issue AI in Industry 4.0)
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31 pages, 5762 KB  
Article
Rarity-Aware Stratified Active Learning for Class-Imbalanced Industrial Object Detection
by Zhor Benhafid and Sid Ahmed Selouani
Appl. Sci. 2026, 16(3), 1236; https://doi.org/10.3390/app16031236 - 26 Jan 2026
Viewed by 653
Abstract
Object detection systems deployed in industrial environments are often constrained by limited annotation budgets, severe class imbalance, and heterogeneous visual conditions. Active learning (AL) aims to reduce labeling costs by selecting informative samples; however, existing strategies struggle to simultaneously ensure robust performance, rare-class [...] Read more.
Object detection systems deployed in industrial environments are often constrained by limited annotation budgets, severe class imbalance, and heterogeneous visual conditions. Active learning (AL) aims to reduce labeling costs by selecting informative samples; however, existing strategies struggle to simultaneously ensure robust performance, rare-class coverage, and stability under realistic industrial constraints. In this work, we propose a rarity-aware, stratified AL framework for industrial object detection that explicitly aligns sample selection with class imbalance and annotation efficiency. The method relies on a composite image-level score that jointly captures model uncertainty, informativeness, and complementary diversity cues, while adaptively emphasizing rare classes. Crucially, a stratified querying mechanism is introduced to explicitly regulate class-wise sample allocation during selection, playing a key role in improving performance stability and rare-class coverage under severe imbalance, without sacrificing global informativeness. The proposed approach operates purely at the data-selection level, making it detector-agnostic and directly applicable to modern object detection pipelines. Experiments conducted on two real-world industrial datasets involving lobster and snow crab parts, using YOLOv10 and YOLOv12, demonstrate improved training stability and annotation efficiency across balanced, imbalanced, and noisy settings over multiple active learning cycles up to 15% labeled data. Complementary comparisons with fully supervised training further show that using only 45–65% of the labeled data is sufficient to retain more than 97% of full-supervision mAP@50 and over 90% of mAP@50:95. Full article
(This article belongs to the Special Issue AI in Industry 4.0)
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18 pages, 1843 KB  
Article
Predicting Human and Environmental Risk Factors of Accidents in the Energy Sector Using Machine Learning
by Kawtar Benderouach, Idriss Bennis, Khalifa Mansouri and Ali Siadat
Appl. Sci. 2026, 16(3), 1203; https://doi.org/10.3390/app16031203 - 24 Jan 2026
Viewed by 534
Abstract
The aim of this article is to develop a machine learning (ML)-based predictive model for industrial accidents in the energy sector. The dataset used in this study was obtained from the Kaggle platform and consists of summaries derived from reports of occupational incidents [...] Read more.
The aim of this article is to develop a machine learning (ML)-based predictive model for industrial accidents in the energy sector. The dataset used in this study was obtained from the Kaggle platform and consists of summaries derived from reports of occupational incidents resulting in injuries or deaths between 2015 and 2017. A total of 4739 accident cases were included, containing information on accident date, accident summary, degree and nature of injury, affected body part, event type, human factors, and environmental factors. Six supervised machine learning models—Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting Decision Trees (GBDT), and Extreme Gradient Boosting (XGBoost)—were developed and compared to identify the most suitable model for the data. Model performance was evaluated using accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC), which were selected to ensure reliable prediction in safety-critical accident scenarios. The results indicate that XGBoost and GBDT achieve superior performance in predicting human and environmental risk factors. These findings demonstrate the potential of machine learning for improving safety management in the energy sector by identifying risk mechanisms, enhancing safety awareness, and providing quantitative predictions of fatal and non-fatal accident occurrences for integration into safety management systems. Full article
(This article belongs to the Special Issue AI in Industry 4.0)
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