Artificial Intelligence in Machine Learning Approaches for Smart Manufacturing Ⅱ

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1060

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Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Alameda de Urquijo s/n, 48013 Bilbao, Spain
Interests: super abrasive machining; milling; manufacturing
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Department of Mechanical Engineering, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain
Interests: mechanical engineering; coatings; machining; manufacturing of aeroengine components
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industry 4.0 is now underway, changing traditional manufacturing processes into smart manufacturing. Smart manufacturing is one of the main industries to make full use of artificial intelligence and machine-learning technologies. Artificial intelligence is making machines smarter than before in the manufacturing industry by addressing how to build computers that improve automatically with experience. This Special Issue is open to new findings and approaches related to the current challenges and opportunities for the applications of artificial intelligence in smart manufacturing. We encourage researchers to contribute to this Special Issue, including, but not being limited to, the following subject areas:

  • Real-time monitoring with machine learning;
  • Artificial intelligence for predictive maintenance;
  • Production scheduling with reinforcement learning;
  • Artificial intelligence and robotics in smart manufacturing;
  • IoT-enabled smart manufacturing;
  • Digital twin-driven smart manufacturing.

Dr. Haizea González-Barrio
Dr. Amaia Calleja-Ochoa
Guest Editors

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Keywords

  • smart manufacturing
  • machine learning
  • digital twins monitoring and control in manufacturing
  • artificial intelligence
  • IoT-enabled smart manufacturing

Published Papers (1 paper)

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Research

19 pages, 3303 KiB  
Article
Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
by José Silva, Pedro Coelho, Luzia Saraiva, Paulo Vaz, Pedro Martins and Alfonso López-Rivero
Appl. Sci. 2024, 14(5), 1850; https://0-doi-org.brum.beds.ac.uk/10.3390/app14051850 - 23 Feb 2024
Viewed by 656
Abstract
Effective quality control is crucial in industrial manufacturing for influencing efficiency, product dependability, and customer contentment. In the constantly changing landscape of industrial production, conventional inspection methods may fall short, prompting the need for inventive approaches to enhance precision and productivity. In this [...] Read more.
Effective quality control is crucial in industrial manufacturing for influencing efficiency, product dependability, and customer contentment. In the constantly changing landscape of industrial production, conventional inspection methods may fall short, prompting the need for inventive approaches to enhance precision and productivity. In this study, we investigate the application of smart glasses for real-time quality inspection during assembly processes. Our key innovation involves combining smart glasses’ video feed with a server-based image recognition system, utilizing the advanced YOLOv8 model for accurate object detection. This integration seamlessly merges mixed reality (MR) with cutting-edge computer vision algorithms, offering immediate visual feedback and significantly enhancing defect detection in terms of both speed and accuracy. Carried out in a controlled environment, our research provides a thorough evaluation of the system’s functionality and identifies potential improvements. The findings highlight that MR significantly elevates the efficiency and reliability of traditional inspection methods. The synergy of MR and computer vision opens doors for future advancements in industrial quality control, paving the way for more streamlined and dependable manufacturing ecosystems. Full article
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