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Advanced Artificial Intelligence Technologies and Applications in Manufacturing and Image Processing

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

Deadline for manuscript submissions: closed (20 May 2025) | Viewed by 586

Special Issue Editor


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Guest Editor
School of Software Engineering, Sun Yat-sen University, Guangzhou 510275, China
Interests: distributed computing; federated computing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As artificial intelligence (AI) evolves, its integration with emerging technologies such as blockchain, edge computing, and the Internet of Things (IoT) is driving innovation across sectors including manufacturing, image processing, healthcare, smart cities, environmental monitoring, and industrial automation. AI systems are transforming various aspects of manufacturing, including design, production, quality control, and supply chain management. AI-driven image recognition is being used for tasks such as seam tracking in fiber laser welding, medical imaging analysis, and various other applications. In healthcare, applications of artificial intelligence include disease prediction, diagnostic support, and precision medicine, amongst others.

This Special Issue mainly focuses on the latest advancements in AI technologies and their transformative applications in the field of manufacturing and image processing. We aim to gather cutting-edge research on topics such as advanced AI algorithms, scalable machine learning models, AI-driven decision-making systems, and secure, decentralized architectures. We invite contributions that explore novel methodologies, practical implementations, and interdisciplinary approaches to addressing real-world challenges using next-generation AI technologies in manufacturing and image processing.

Dr. Jianguo Chen
Guest Editor

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • intelligent manufacturing
  • image processing
  • AI algorithms
  • machine learning

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Published Papers (1 paper)

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Research

23 pages, 4630 KiB  
Article
Federated Learning-Based Framework to Improve the Operational Efficiency of an Articulated Robot Manufacturing Environment
by Junyong So, In-Bae Lee and Sojung Kim
Appl. Sci. 2025, 15(8), 4108; https://doi.org/10.3390/app15084108 - 8 Apr 2025
Viewed by 339
Abstract
Although articulated robots with flexible automation systems are essential for implementing smart factories, their high initial investment costs make them difficult for small and medium-sized enterprises to implement. This study proposes a federated learning-based articulated robot control framework to improve the task completion [...] Read more.
Although articulated robots with flexible automation systems are essential for implementing smart factories, their high initial investment costs make them difficult for small and medium-sized enterprises to implement. This study proposes a federated learning-based articulated robot control framework to improve the task completion of multiple articulated robots used in automated systems under limited computing resources. The proposed framework consists of two modules: (1) a federated learning module for the cooperative training of multiple joint robots on a part-picking task and (2) an articulated robot control module to balance the efficiency of limited resources. The proposed framework is applied to cases with different numbers of joint robots, and its performance is evaluated in terms of training completion time, resource share ratio, network traffic, and completion time of a picking task. Under the devised framework, the experiment demonstrates object recognition by three joint robots with an accuracy of approximately 80% at a minimum number of learning rounds of 76 and with a network traffic intensity of 2303.5 MB. As a result, this study contributes to the expansion of federated learning use for articulated robot control in limited environments, such as small and medium-sized enterprises. Full article
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