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Processes
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Revolutionizing Smart Manufacturing: Cutting-Edge Technologies in the Industry's Future
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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 2391

Special Issue Editors

Dr. Gangfeng Wang

E-Mail Website
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
Dr. Qichao Jin

E-Mail Website
Guest Editor
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (2 papers)

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Research

23 pages, 3849 KB  
Article
Multi-AGV Collaborative Task Scheduling and Deep Reinforcement Learning Optimization Under Multi-Feature Constraints
by Dongping Zhao, Hui Li, Ziyang Wang and Hang Li
Processes 2025, 13(11), 3754; https://doi.org/10.3390/pr13113754 - 20 Nov 2025
Viewed by 477
Abstract
To address the challenges of low efficiency, instability, and difficulties in meeting multiple constraints simultaneously in multi-AGV (Automated Guided Vehicle) task scheduling for intelligent manufacturing and logistics, this paper introduces a scheduling method based on multi-feature constraints and an improved deep reinforcement learning [...] Read more.
To address the challenges of low efficiency, instability, and difficulties in meeting multiple constraints simultaneously in multi-AGV (Automated Guided Vehicle) task scheduling for intelligent manufacturing and logistics, this paper introduces a scheduling method based on multi-feature constraints and an improved deep reinforcement learning (DRL) approach (Improved Proximal Policy Optimization, IPPO). The method integrates multiple constraints, including minimizing task completion time, reducing penalty levels, and minimizing scheduling time deviation, into the scheduling optimization process. Building on the conventional PPO algorithm, several enhancements are introduced: a dynamic penalty mechanism is implemented to adaptively adjust constraint weights, a structured reward function is designed to boost learning efficiency, and sampling bias correction is combined with global state awareness to improve training stability and global coordination. Simulation experiments demonstrate that, after 10,000 iterations, the minimum task completion time drops from 98.2 s to 30 s, the penalty level decreases from 130 to 82, and scheduling time deviation reduces from 12 s to 0.5 s, representing improvements of 69.4%, 37%, and 95.8% in the same scenario, respectively. Compared to genetic algorithms (GAs) and rule-based scheduling methods, the IPPO approach demonstrates significant advantages in average task completion time, total system makespan, and overall throughput, along with faster convergence and better stability. These findings demonstrate that the proposed methodology enables effective multi-objective collaborative optimization and efficient task scheduling within complex dynamic environments, holding significant value for intelligent manufacturing and logistics systems. Full article
(This article belongs to the Special Issue Revolutionizing Smart Manufacturing: Cutting-Edge Technologies in the Industry's Future)
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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 - 5 Sep 2025
Viewed by 1304
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
(This article belongs to the Special Issue Revolutionizing Smart Manufacturing: Cutting-Edge Technologies in the Industry's Future)
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