Revolutionizing Smart Manufacturing: Cutting-Edge Technologies in the Industry's Future

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 324

Special Issue Editors


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Guest Editor
Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China
Interests: smart manufacturing; manufacturing informatics; innovation design; knowledge graph; intelligent control; intelligent construction machinery
Key Laboratory of Road Construction Technology and Equipment of MOE, Chang’an University, Xi’an 710064, China
Interests: smart machine tool; smart manufacturing; intelligent manufacturing process

Special Issue Information

Dear Colleagues,

Driven by the convergence of Industry 4.0 technologies, the manufacturing landscape is undergoing a profound transformation. Artificial intelligence (AI), the Internet of Things (IoT), smart manufacturing, virtual simulation, advanced robotics, and big data analytics are fundamentally reshaping production processes, supply chains, and product innovation. To maintain competitiveness and sustainability, industries urgently need to adopt and integrate these cutting-edge technologies, moving towards more agile, intelligent, efficient, and resilient manufacturing paradigms.

This Special Issue on “Revolutionizing Smart Manufacturing: Cutting-Edge Technologies in the Industry's Future” seeks high-quality, original research articles, reviews, and case studies focusing on the forefront of intelligent production systems. We cordially invite contributions exploring novel applications and breakthroughs in key enabling technologies. Topics include, but are not limited to, the following:

  • Smart production system planning and optimization;
  • Application of digital twins in product design, manufacturing, assembly, testing and maintenance;
  • AI and machine learning for predictive maintenance, quality control, and optimization;
  • Intelligent process design and simulation for complex products;
  • Innovation design and optimization of engineering equipments;
  • Industrial knowledge graph and manufacturing semantics;
  • Advanced robotics and collaborative systems;
  • Sustainable and green manufacturing enabled by smart technologies.

Dr. Gangfeng Wang
Dr. Qichao Jin
Guest Editors

Manuscript Submission Information

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Keywords

  • advanced manufacturing
  • artificial intelligence
  • precision machining
  • intelligent assembly
  • knowledge engineering
  • digital twin
  • optimization design
  • advanced robot
  • intelligent equipment

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Published Papers (1 paper)

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Research

17 pages, 7391 KB  
Article
Research on a Lightweight Textile Defect Detection Algorithm Based on WSF-RTDETR
by Jun Chen, Shubo Zhang, Yingying Yang, Weiqian Li and Gangfeng Wang
Processes 2025, 13(9), 2851; https://doi.org/10.3390/pr13092851 (registering DOI) - 5 Sep 2025
Viewed by 134
Abstract
Textile defect detection technology has become a core component of industrial quality control. With the advancement of artificial intelligence technologies, an increasing number of intelligent recognition methods are being actively researched and deployed in the textile defect detection. To further improve detection accuracy [...] Read more.
Textile defect detection technology has become a core component of industrial quality control. With the advancement of artificial intelligence technologies, an increasing number of intelligent recognition methods are being actively researched and deployed in the textile defect detection. To further improve detection accuracy and quality, we propose a new lightweight process named WSF-RTDETR with reduced computational resources. Firstly, we integrated WTConv convolution with residual blocks to form a lightweight WTConv-Block module, which could enhance the capability of capturing detailed features of tiny defective targets while reducing computational overhead. Subsequently, a lightweight slimneck-SSFF feature fusion architecture was constructed to enhance the feature fusion performance. In addition, the Focaler–MPDIoU loss function was presented by incorporating dynamic weight adjustment and multi-scale perception mechanism, which could improve the detection accuracy and convergence speed for tiny defective targets. Finally, we conducted experiments on a textile defect dataset to further validate the effectiveness of the WSF-RTDETR model. The results demonstrate that the model improves mean average precision (mAP50) by 4.71% while reducing GFLOPs and the number of parameters by 24.39% and 31.11%, respectively. The improvements in both detection performance and computational efficiency would provide an effective and reliable solution for industrial textile defect detection. Full article
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