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2nd Edition of Control Design and Numerical Computation in Manufacturing...
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2nd Edition of Control Design and Numerical Computation in Manufacturing Process System

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

Deadline for manuscript submissions: 20 July 2026 | Viewed by 2555

Special Issue Editors

Prof. Dr. Yifei Tong

E-Mail Website
Guest Editor
School of Mechanical Engineering, Nanjing University of Science & Technology, Nanjing 210094, China
Interests: production process control and optimization; smart manufacturing; digital twin
Special Issues, Collections and Topics in MDPI journals
Dr. Fengque Pei

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213000, China
Interests: collaborative optimization; process control; industral big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The first edition of this Special Issue, entitled “Control Design and Numerical Computation in Manufacturing Process System”, collected nine insightful papers that attracted more than 10,000+ views. Due to the considerable interest in this topic, we propose a second edition of this Special Issue, entitled “2nd Edition of Control Design and Numerical Computation in Manufacturing Process System".

As a result of the growing emphasis on decision-making, control system design and numerical computation are attracting increased interest from the industrial community, influencing design, manufacturing, assembly, operation, and maintenance processes. A control system includes a generalized decision support system, intelligent decision system, process control system, etc. Using traditional simulation and new IT (such as digital twin and big data technology), numerical computation is widely used in innovative methods and new process applications.

This Special Issue on “2nd Edition of Control Design and Numerical Computation in Manufacturing Process System” aims to curate novel advances in developing and applying process control and numerical computation. Potential topics include (but are not limited to):

  • Control system design, including the design application of decision support systems, intelligent decision systems, and process control systems.
  • Numerical computation, including the application of digital twin technology, big data analysis technology, etc.
  • Equipment design and control, including industrial equipment, agricultural equipment, etc.
  • Manufacturing systems design, control, and optimization, including the application of production, assembly, and distribution processes.

Prof. Dr. Yifei Tong
Dr. Fengque Pei
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

  • control system design
  • numerical computation
  • equipment design and control
  • manufacturing systems design, control, and optimization
  • process design and control

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

27 pages, 6086 KB  
Article
Application of Hybrid Cellular Automata Method for High-Precision Transient Stiffness Design of a Press Machine Frame
by Zeqi Tong, Chenlei Lin, Feng Li and Tingting Chen
Processes 2025, 13(11), 3726; https://doi.org/10.3390/pr13113726 - 18 Nov 2025
Viewed by 363
Abstract
It is crucial to investigate methods for improving the stiffness performance of machine tools according to their specific dynamic working conditions. This paper presents a complete computer-aided workflow for structural transient topology optimization (TO) design, which is applied to the structural design issue [...] Read more.
It is crucial to investigate methods for improving the stiffness performance of machine tools according to their specific dynamic working conditions. This paper presents a complete computer-aided workflow for structural transient topology optimization (TO) design, which is applied to the structural design issue of the JH31-250 press machine (Zhejiang Weili Forging Machinery Co., Ltd., Shaoxing, China). The stiffness influenced by the shape of the press frame under long-term dynamic impact load is analyzed, and an optimal design for the frame structure of the press machine is explored. In order to reduce the iteration time of the dynamic analysis, we also proposed a way to simplify the physical structure of the machine tool into a thin-walled structure model with artificial pseudo-density and introduced the hybrid cellular automata (HCA) criterion to obtain the topological iteration direction. This simplified model can be transformed back into 3D solid design of the press. The maximum relative displacement of the worktable in this optimized press model is 0.4896 mm, which is reduced by 31.02% compared to the original press model, which shows that the transient dynamic stiffness of the press machine frame is improved. This work presents a topological optimization method and path, which can be used for the optimization of dynamic stiffness in forging machine tools, and proves the correctness and effectiveness of the design for the transient dynamic stiffness of the frame. Full article
(This article belongs to the Special Issue 2nd Edition of Control Design and Numerical Computation in Manufacturing Process System)
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21 pages, 7886 KB  
Article
Identification and Posture Evaluation of Effective Tea Buds Based on Improved YOLOv8n
by Pan Wang, Tingting He, Luxin Xie, Wenyu Yi, Lei Zhao, Chunxia Wang, Jiani Wang, Zhiye Bai and Song Mei
Processes 2025, 13(11), 3658; https://doi.org/10.3390/pr13113658 - 11 Nov 2025
Viewed by 387
Abstract
Aiming at the low qualification rate and high damage caused by the lack of identification, localization, and posture estimation of tea buds in the mechanical harvesting process of famous tea, a framework of lightweight detection + PCA-skeleton fusion posture estimation was proposed. Based [...] Read more.
Aiming at the low qualification rate and high damage caused by the lack of identification, localization, and posture estimation of tea buds in the mechanical harvesting process of famous tea, a framework of lightweight detection + PCA-skeleton fusion posture estimation was proposed. Based on the YOLOv8n model, the StarNet backbone network was introduced to enable lightweight detection, and the ASF-YOLO multi-scale attention module was embedded to improve the feature fusion ability. Based on the detection frame, the GrabCut-Watershed fusion segmentation was employed to obtain the bud mask. Combined with PCA and skeleton extraction algorithms, the main direction deviations of bent buds and clasped leaves were solved by Bézier curve fitting, and the morphology–posture dual-factor scoring model was thereby constructed to realize the picking ranking. Compared with the original YOLOv8n model, the results showed that the detection accuracy and mAP50 of the Improved model decreased to 85.6% and 90.5%, respectively, and the recall rate increased to 81.7%. Meanwhile, the calculation load of the improved model was reduced by 23.6%, reaching 6.8 GFLOPs, indicating a significant improvement in lightweight. The morphology–posture dual-factor scoring model achieved a score of 0.88 for a single bud in vertical direction (θ ≈ 90°), a score of approximately 0.66–0.71 for buds with partially unfolded leaves and slightly bent buds, and a score of 0.48–0.53 for severely bent and overlapped buds. The results of this study have the potential to guide the picking robotic arms to preferentially pick tea buds with high adaptability and provide a reliable visual solution for low-loss and high-efficiency mechanized harvesting of famous tea in complex tea gardens. Full article
(This article belongs to the Special Issue 2nd Edition of Control Design and Numerical Computation in Manufacturing Process System)
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21 pages, 3639 KB  
Article
Research on Data Prediction Model for Aerodynamic Drag Reduction Effect in Platooning Vehicles
by Zhexin Wang, Xuepeng Guo, Ning Yang, Lingjun Su, Lu’an Chen, Zhao Zhang and Chengyu Zhu
Processes 2025, 13(7), 2056; https://doi.org/10.3390/pr13072056 - 28 Jun 2025
Viewed by 1417
Abstract
With the development of intelligent transportation systems, platooning can reduce vehicle aerodynamic drag by decreasing spacing between vehicles, improving transportation efficiency and reducing emissions. However, it is difficult for existing models to enable dynamic adjustment and real-time feedback. Therefore, this study proposes a [...] Read more.
With the development of intelligent transportation systems, platooning can reduce vehicle aerodynamic drag by decreasing spacing between vehicles, improving transportation efficiency and reducing emissions. However, it is difficult for existing models to enable dynamic adjustment and real-time feedback. Therefore, this study proposes a digital twin system for real-time drag coefficient prediction using stacking ensemble learning. First, 2000 datasets of pressure distributions and drag coefficients under varying spacings were obtained through simulations. Then, an online prediction model for the aerodynamic performance of platooning vehicles was then constructed, realizing real-time drag coefficient prediction, and verifying the model performance using computational fluid dynamics data. The results indicate that the model proposed achieves 98.56% prediction accuracy, significantly higher than that of the traditional BP model (75.78%), and effectively captures the nonlinear relationship between vehicle spacing and drag coefficient. The influence mechanism of vehicle spacing on the aerodynamic performance of platooning vehicles revealed in this study enables high-precision real-time prediction under dynamic parameters. Full article
(This article belongs to the Special Issue 2nd Edition of Control Design and Numerical Computation in Manufacturing Process System)
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