Application of Artificial Intelligence in Smart Factories: From Sensor Networks to Large Language Models

A special issue of Designs (ISSN 2411-9660). This special issue belongs to the section "Smart Manufacturing System Design".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 317

Special Issue Editors

Artificial Intelligence and Big Data Department, Endicott College, Woosong University, Daejeon 34606, Republic of Korea
Interests: digital signal processing; image processing; multimedia systems; parallel programming; fault diagnosis; IoT smart devices; data communication and networks; microprocessors; computer architecture
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Guest Editor
Human and Digital Interface Department, JW Kim College of Future Studies, Woosong University, Daejeon 34606, Republic of Korea
Interests: AI for smart factories; AI for health

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has emerged as a transformative force in smart manufacturing, revolutionizing traditional factory operations through intelligent automation, predictive maintenance, and data-driven decision-making. With advancements in sensor technologies, edge computing, and large language models, AI-driven approaches are reshaping industrial processes from shop floor operations to supply chain optimization.

This Special Issue aims to explore the latest developments in AI applications within smart factory environments, with a particular focus on industrial IoT, predictive systems, and intelligent process optimization. Topics of interest include, but are not limited to, the following:

  • Real-time sensor data analytics and edge AI for industrial equipment (e.g., wind turbines, electric motors, and industrial pumps);
  • Predictive maintenance and anomaly detection using machine learning in manufacturing systems;
  • Large language models (LLMs) for industrial documentation, knowledge management, and operator assistance;
  • Digital twin technology for production line optimization and virtual commissioning;
  • AI-driven quality control and defect detection in manufacturing processes;
  • Smart energy management and optimization in industrial settings;
  • Industrial robotics and computer vision for automated inspection and assembly;
  • Integration of AI with industrial control systems (ICSs) and SCADA networks. 

We invite researchers and practitioners to contribute original research articles, review papers, and case studies showcasing novel AI applications in smart factory environments. Specific industrial use cases are encouraged, including the following:

  • Predictive maintenance systems for wind turbine farms;
  • LLM-powered troubleshooting assistants for machine operators;
  • Computer vision systems for quality control in automotive manufacturing;
  • Energy optimization systems for motor-driven industrial processes;
  • Sensor fusion and anomaly detection in chemical processing plants.

Through this Special Issue, we aim to provide a platform for sharing practical implementations and theoretical advances in AI-driven smart manufacturing, bridging the gap between academic research and industrial applications.

Dr. Jia Uddin
Dr. Mahe Zabin
Guest Editors

Manuscript Submission Information

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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. Designs is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

  • artificial intelligence (AI)
  • machine learning
  • modeling and simulation
  • digital twins
  • computer-aided design (CAD)
  • LLM
  • AI systems

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Published Papers (1 paper)

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Research

20 pages, 2239 KiB  
Article
A Novel Lightweight Deep Learning Approach for Drivers’ Facial Expression Detection
by Jia Uddin
Designs 2025, 9(2), 45; https://doi.org/10.3390/designs9020045 - 3 Apr 2025
Viewed by 236
Abstract
Drivers’ facial expression recognition systems play a pivotal role in Advanced Driver Assistance Systems (ADASs) by monitoring emotional states and detecting fatigue or distractions in real time. However, deploying such systems in resource-constrained environments like vehicles requires lightweight architectures to ensure real-time performance, [...] Read more.
Drivers’ facial expression recognition systems play a pivotal role in Advanced Driver Assistance Systems (ADASs) by monitoring emotional states and detecting fatigue or distractions in real time. However, deploying such systems in resource-constrained environments like vehicles requires lightweight architectures to ensure real-time performance, efficient model updates, and compatibility with embedded hardware. Smaller models significantly reduce communication overhead in distributed training. For autonomous vehicles, lightweight architectures also minimize the data transfer required for over-the-air updates. Moreover, they are crucial for their deployability on hardware with limited on-chip memory. In this work, we propose a novel Dual Attention Lightweight Deep Learning (DALDL) approach for drivers’ facial expression recognition. The proposed approach combines the SqueezeNext architecture with a Dual Attention Convolution (DAC) block. Our DAC block integrates Hybrid Channel Attention (HCA) and Coordinate Space Attention (CSA) to enhance feature extraction efficiency while maintaining minimal parameter overhead. To evaluate the effectiveness of our architecture, we compare it against two baselines: (a) Vanilla SqueezeNet and (b) AlexNet. Compared with SqueezeNet, DALDL improves accuracy by 7.96% and F1-score by 7.95% on the KMU-FED dataset. On the CK+ dataset, it achieves 8.51% higher accuracy and 8.40% higher F1-score. Against AlexNet, DALDL improves accuracy by 4.34% and F1-score by 4.17% on KMU-FED. Lastly, on CK+, it provides a 5.36% boost in accuracy and a 7.24% increase in F1-score. These results demonstrate that DALDL is a promising solution for efficient and accurate emotion recognition in real-world automotive applications. Full article
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