Advances in Industry 4.0 Technologies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 388

Special Issue Editors


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Guest Editor
Department of Mechanical Engineering, Chung Yuan Christian University, Taoyuan 320314, Taiwan
Interests: smart manufacturing; AI; smart material; sustainability; semiconductor equipment

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Guest Editor
Department of Industrial and Systems Engineering, Chung Yuan Christian University, Taoyuan 320, Taiwan
Interests: ergonomics; design of innovative human–machine interfaces; new product design; smart manufacturing; machine learning

Special Issue Information

Dear Colleagues,

Industry 4.0 marks a significant transformation in manufacturing and production, characterized by the convergence of digital and physical systems. This era is driven by advancements in smart factories, cyber–physical systems, the Internet of Things (IoT), big data analytics, artificial intelligence (AI), and machine learning. These technologies are redefining traditional manufacturing processes, enhancing efficiency, flexibility, and responsiveness. The significance of this research area lies in its potential to substantially boost productivity, reduce operational costs, and create new business opportunities across various sectors.

This Special Issue aims to provide a comprehensive overview of the latest advancements in Industry 4.0 technologies, exploring their applications, benefits, and challenges. It seeks to contribute to the ongoing evolution of manufacturing and production systems by showcasing innovative research and practical implementation.

Original research articles and reviews are welcome for this Special Issue. Research areas may include, but are not limited to, the following themes:

  • Development and implementation of smart factories;
  • Innovations in cyber–physical systems;
  • Applications of the Internet of Things (IoT) in manufacturing;
  • Big data analytics for production optimization;
  • Advances in artificial intelligence (AI) and machine learning for industrial applications;
  • Automation technologies and their impact on efficiency;
  • Cybersecurity challenges and solutions in Industry 4.0;
  • Workforce training and skill development for Industry 4.0.

Dr. Hariyanto Gunawan
Dr. Yung-Tsan Jou
Guest Editors

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Keywords

  • Industry 4.0
  • machine learning
  • Internet of Things
  • digital twin
  • cybersecurity

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Published Papers (1 paper)

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Research

27 pages, 7988 KiB  
Article
Enhanced Computer Numeric Controller Milling Efficiency via Air-Cutting Minimization Using Logic-Based Benders Decomposition Method
by Hariyanto Gunawan, Didik Sugiono, Ren-Qi Tu, Wen-Ren Jong and AM Mufarrih
Electronics 2025, 14(13), 2613; https://doi.org/10.3390/electronics14132613 (registering DOI) - 28 Jun 2025
Abstract
In computer numeric controller (CNC) milling machining, air-cutting, where the tool moves without engaging the material, will reduce the machining efficiency. This study proposes a novel methodology to detect and minimize non-productive (air-cutting) time in real-time using spindle load monitoring, vibration signal analysis, [...] Read more.
In computer numeric controller (CNC) milling machining, air-cutting, where the tool moves without engaging the material, will reduce the machining efficiency. This study proposes a novel methodology to detect and minimize non-productive (air-cutting) time in real-time using spindle load monitoring, vibration signal analysis, and NC code tracking. A logic-based benders decomposition (LBBD) approach was used to optimize toolpath segments by analyzing air-cutting occurrences and dynamically modifying the NC code. Two optimization strategies were proposed: increasing the feedrate during short air-cutting segments and decomposing longer segments using G00 and G01 codes with positioning error compensation. A human–machine interface (HMI) developed in C# enables real-time monitoring, detection, and minimization of air-cutting. Experimental results demonstrate up to 73% reduction of air-cutting time and up to 42% savings in total machining time, validated across multiple scenarios with varying cutting parameters. The proposed methodology offers a practical and effective solution to enhance CNC milling productivity. Full article
(This article belongs to the Special Issue Advances in Industry 4.0 Technologies)
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