Submit to Processes Review for Processes Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

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

Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A Special Issue of Processes (ISSN 2227-9717) belonging to the section "Manufacturing Processes and Systems".

Deadline for manuscript submissions: closed (20 July 2026) | Viewed by 9038

Share This 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-anonymized 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 semimonthly 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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (9 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

18 pages, 1840 KB

Open AccessArticle

Manufacturing Service Composition Optimization for Coating Equipment Wallboards Using an Improved NSGA-III Algorithm

by Jing Xu, Feng Ren, Ming Zhang, Hongen Yang, Zirui Zhao and Shanhui Liu

Processes 2026, 14(15), 2425; https://doi.org/10.3390/pr14152425 - 27 Jul 2026

Viewed by 375

Abstract

To address the low production efficiency and insufficient cross-enterprise collaboration in wallboard outsourcing for coating equipment manufacturing, this study proposes a wallboard manufacturing service composition optimization method based on an improved NSGA-III algorithm. First, the service composition optimization problem in wallboard manufacturing is analyzed, and a mathematical model for wallboard outsourcing service composition optimization is established. Next, an improved NSGA-III method integrating Latin hypercube sampling, greedy local search, and Lévy flight-based global search strategies is proposed, alongside a similarity measurement method for the objective-space structure based on statistical features and distribution differences, which is used to select benchmark functions that closely match the characteristics of actual business data. Finally, comparative experiments using benchmark functions and real-world business data are conducted to verify the effectiveness and superiority of the proposed method in solving service composition optimization problems. The experimental results demonstrate that the proposed method achieves excellent performance in both solution quality and computational efficiency, significantly improving the utilization of wallboard outsourcing manufacturing resources across enterprises and facilitating efficient business process flows. Full article

(This article belongs to the Special Issue 2nd Edition of Control Design and Numerical Computation in Manufacturing Process System)

► Show Figures

Figure 1

35 pages, 4393 KB

Open AccessArticle

A-PPO-Based Scheduling Optimization for Phase-Oriented Complex-Product Manufacturing Workshops

by Ganlong Wang, Yue Wang, Yanxia Wu and Guoyin Zhang

Processes 2026, 14(14), 2318; https://doi.org/10.3390/pr14142318 - 16 Jul 2026

Viewed by 343

Abstract

Complex-product manufacturing workshops are characterized by diverse process routes, heterogeneous machine capabilities, tight coupling between processing and assembly, and stringent parent–child kitting constraints. These characteristics make fixed-priority rules insufficient for coordinating job release, machine competition, and assembly waiting. To address scheduling in a production process that connects front-end processing with back-end fixed-position assembly, this study proposes an attention-based proximal policy optimization method. First, the manufacturing process is formulated as a staged model comprising a front-end hybrid flow-shop processing stage and a back-end fixed-position assembly stage. The model captures operation precedence, machine heterogeneity, stage transitions, and kitting constraints. Next, a reinforcement-learning scheduling framework is established by defining the state space, action space, and dynamic action mask, thereby incorporating operation sequences, machine eligibility, resource occupancy, and assembly release constraints into sequential decision making. Furthermore, an A-PPO policy that combines local operation attention with machine-competition attention is designed to select feasible job–equipment-unit matching actions. An industrial engineering case shows that A-PPO achieves a makespan of 3823.7, outperforming six fixed-priority rules (Rule 2–Rule 7) and a genetic algorithm (GA) baseline. GA achieves a makespan of 3875.3, whereas the best rule-based methods achieve 3986.4. Compared with GA, A-PPO reduces the makespan by 1.33%; compared with Rule 6 and Rule 7, it reduces the makespan by 4.08%; and compared with the average of the six rules, it reduces the makespan by 18.95%. The results demonstrate that the proposed method shortens order completion cycles and supports intelligent scheduling in complex-product manufacturing workshops. The proposed method is currently applicable to phase-oriented complex-product manufacturing workshops with structured process routes, equipment capabilities, and parent–child assembly constraints; its transferability across layouts and enterprises requires further validation. Full article

(This article belongs to the Special Issue 2nd Edition of Control Design and Numerical Computation in Manufacturing Process System)

► Show Figures

Figure 1

33 pages, 5774 KB

Open AccessArticle

Multi-Objective Optimization of Multi-Cooperative Agricultural Machinery Scheduling Under Continuous Workload Sharing: A Hybrid Particle Swarm–Tabu Search Approach

by Weimin Wang, Shenghai Qiu, Jia Chen and Qinghai Jiang

Processes 2026, 14(13), 2181; https://doi.org/10.3390/pr14132181 - 3 Jul 2026

Viewed by 360

Abstract

Coordinating a shared fleet across multiple owners under tight time windows is a challenging multi-objective problem balancing cost, timeliness, and equity. We study it for multi-cooperative agricultural machinery dispatch, formulating the Multi-Cooperative Agricultural Machinery Scheduling Problem under Continuous Workload Sharing (MAMSP-CWS) as a three-objective model that minimizes inter-area transfer cost, time-window violation, and cross-cooperative workload imbalance. To approximate the Pareto front, we develop a Multi-Objective Hybrid Particle Swarm Optimization with Tabu Search and Sparsity Repair (MO-HPSO-TS-SR), which couples particle-swarm search, tabu-search refinement, and a sparsity-repair operator within an external crowding-distance archive. The method is evaluated on three scales (a real instance from Liyang, China, and two synthetic ones) against NSGA-II-CWS and HTSMOGA-CWS over 20 independent runs each. MO-HPSO-TS-SR attains the best mean value on every metric-by-scale combination, with a decisive convergence advantage (hypervolume and IGD; Holm-adjusted

p < 0.001

, Cliff’s

δ \approx 1

). A mechanism decomposition identifies Sparsity Repair as the dominant contributor to hypervolume, with Tabu Search as a complementary refiner. The advantage over NSGA-II-CWS widens with problem scale, from 19.4% on the Small instance to 74.2% on the Large instance, reflecting the disproportionate degradation of the genetic baseline rather than a growing advantage of the proposed method. Beyond agriculture, the framework extends to other continuous-encoding scheduling problems, providing a transferable decision-support tool. Full article

(This article belongs to the Special Issue 2nd Edition of Control Design and Numerical Computation in Manufacturing Process System)

► Show Figures

Figure 1

17 pages, 2396 KB

Open AccessArticle

Model Linearization and Stability of Marine Mooring Winches

by Wencheng Lin and Qingpeng Chen

Processes 2026, 14(11), 1781; https://doi.org/10.3390/pr14111781 - 29 May 2026

Viewed by 323

Abstract

The tension of a marine winch rope depends on the hydraulic pressure supplied to its input hydraulic motor. Traditionally, winches employ a relief valve to control the oil pressure of hydraulic motors. Owing to the inherent control characteristics of the relief valve, this control mode leads to continuous fluctuations in the system oil pressure, causing severe variations in the rope tension during operation. In this study, a direct-acting three-way proportional pressure-reducing valve was used to control the oil pressure of the winch, ensuring that the input pressure to the hydraulic motor was maintained at a set value, thereby mitigating the risk of drastic fluctuations in rope tension during vessel mooring. However, proportional pressure-reducing valve control exhibits shortcomings, such as static nonlinearities, insufficient dynamic response, and poor anti-interference stability, leading to oscillations in the outlet oil pressure and resulting in rope tension fluctuations in the winch. Based on the force and flow balance equations of the proportional pressure-reducing valve and in conjunction with the load characteristics of the winch, a mathematical model of the winch control system was established. An operating point for the pressure-reducing valve was determined, and the control system model was linearized. According to the Bode plot and frequency-domain index analysis, four key parameters affecting the outlet pressure fluctuation of the pressure-reducing valve were identified (valve port flow gain coefficient, viscous damping coefficient, transient hydraulic damping coefficient, and hydraulic spring stiffness). From the perspective of winch operation management, the working parameters of the hydraulic system were adjusted accordingly, and their effects on the four key parameters were analyzed. The results, in combination with model linearization and Bode plot analysis, indicate that appropriately lowering the operating temperature of the hydraulic oil can effectively improve the frequency-domain indices and stability margin of the control system, significantly enhancing the relative stability of the marine winch rope tension. Full article

(This article belongs to the Special Issue 2nd Edition of Control Design and Numerical Computation in Manufacturing Process System)

► Show Figures

Figure 1

28 pages, 1486 KB

Open AccessArticle

Scheduling Optimization of Special Cable Production Workshop with AMR Constraints

by Zhen Ni, Yalin Wang, Yifei Tong and Hao Zhang

Processes 2025, 13(12), 3992; https://doi.org/10.3390/pr13123992 - 10 Dec 2025

Viewed by 915

Abstract

Material handling in special cable manufacturing remains highly inefficient, with manual logistics accounting for nearly 90% of product cycle time. Existing scheduling methods commonly rely on oversimplified assumptions and fail to integrate machine processing with autonomous mobile robot (AMR) transportation constraints, limiting practical applicability. This study proposes a comprehensive scheduling framework that explicitly incorporates AMR movement dynamics—covering empty-load travel and loaded transportation—into flexible job shop scheduling. A dual-objective model is formulated to minimize makespan and total equipment load, providing a more realistic evaluation of workshop performance. To solve this model, an enhanced Sparrow Search Algorithm (SSA) is developed, featuring Pareto dominance sorting, harmonic mean crowding, an external elite archive, and adaptive discoverer–follower scaling to improve convergence stability and avoid premature stagnation. Using real production data from a cable workshop, the proposed method achieves a 15.0% reduction in completion time and a 36.3% reduction in equipment load compared with the traditional SSA. The results demonstrate that the integrated model and improved algorithm offer an effective solution for AMR-constrained multi-objective workshop scheduling. Full article

(This article belongs to the Special Issue 2nd Edition of Control Design and Numerical Computation in Manufacturing Process System)

► Show Figures

Figure 1

27 pages, 6086 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1031

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 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)

► Show Figures

Figure 1

21 pages, 7886 KB

Open AccessArticle

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 978

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 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)

► Show Figures

Figure 1

21 pages, 3639 KB

Open AccessArticle

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

Cited by 1 | Viewed by 2706

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 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)

► Show Figures

Figure 1

Review

Jump to: Research

26 pages, 1299 KB

Open AccessReview

Mathematical Morphology-Based Fault Diagnosis for Rotating Machinery: A Review

by Tingkai Gong, Xiaohui Yuan, Bing Ji and Zhinong Li

Processes 2026, 14(4), 650; https://doi.org/10.3390/pr14040650 - 13 Feb 2026

Viewed by 1106

Abstract

Rotating machinery is a crucial element of mechanical equipment, and during serving life, failures are inevitable due to human and non-human factors. Signal processing techniques serve as essential tools for diagnosing such faults. Among them, mathematical morphology (MM) has attracted considerable research interest in this domain owing to nonlinear filtering, simple computation rules and well-established theoretical foundation. Thus, numerous papers have been published in academic journals and conference proceedings. This review paper attempts to outline the morphological framework and to summarize these applications focusing on rolling element bearings and gears. Finally, it provides an analysis of the relevant discussions on MM, and suggests several potential prospects. Full article

(This article belongs to the Special Issue 2nd Edition of Control Design and Numerical Computation in Manufacturing Process System)

► Show Figures

Journal Menu

Journal Browser

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

Share This Special Issue

Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (9 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI