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Processes
Special Issues
Control Design and Numerical Computation in Manufacturing Process System
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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: closed (30 September 2024) | Viewed by 7925

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
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
Dr. Yuliya Sergeevna Ivanova

E-Mail Website
Guest Editor
Bauman Moscow State Technical University, 5 Second Baumanskaya Street, Moscow 105005, Russia
Interests: machine durability and reliability; design and manufacturing systems for increasing wear resistance of the parts surface layer; equipment design and control

Special Issue Information

Dear Colleagues,

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. Numerical computation, using traditional simulation and the New IT (such as digital twin and big data technology), is widely mentioned in innovative methods and new process applications.

This Special Issue on “Control Design and Numerical Computation in Manufacturing Process System” aims to curate novel advances in the development and application of 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
Dr. Yuliya Sergeevna Ivanova
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 polices can be found here.

Published Papers (7 papers)

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Research

15 pages, 4769 KiB  
Article
Using the Methods-Time Measurement Calculator to Determine the Time Norms for Technological Sewing Operations in the Clothing Industry
by Snježana Kirin, Damir Kralj and Anica Hursa Šajatović
Processes 2024, 12(8), 1763; https://doi.org/10.3390/pr12081763 - 21 Aug 2024
Viewed by 627
Abstract
The work in the technological sewing process is carried out on machine systems characterised by machine–hand work, where the worker and the machine work simultaneously. Such a work system requires a high level of responsibility in terms of quality, quantity, and the correct [...] Read more.
The work in the technological sewing process is carried out on machine systems characterised by machine–hand work, where the worker and the machine work simultaneously. Such a work system requires a high level of responsibility in terms of quality, quantity, and the correct and timely execution of work, which requires workers to have fast and accurate reflexes, as well as exceptionally good psychomotor and visual skills. By applying the basic movements of the Methods-Time Measurement (MTM) system, the elaborated standard sets of hand sub-operations included in technological sewing operations, and the method of determining normal times for straight seams (RAV) and curved seams (ZAK), it is possible to determine the working method and time norm of individual technological sewing operations. The MTM Calculator software was developed to facilitate the determination of working methods and time norms on the basis of the MTM system. It can be used to quickly calculate the production time for a technological sewing operation. Full article
(This article belongs to the Special Issue Control Design and Numerical Computation in Manufacturing Process System)
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20 pages, 7125 KiB  
Article
Research on Real-Time Prediction Method of Photovoltaic Power Time Series Utilizing Improved Grey Wolf Optimization and Long Short-Term Memory Neural Network
by Xinyi Lu, Yan Guan, Junyu Liu, Wenye Yang, Jiayin Sun and Jing Dai
Processes 2024, 12(8), 1578; https://doi.org/10.3390/pr12081578 - 28 Jul 2024
Cited by 1 | Viewed by 711
Abstract
This paper proposes a novel method for the real-time prediction of photovoltaic (PV) power output by integrating phase space reconstruction (PSR), improved grey wolf optimization (GWO), and long short-term memory (LSTM) neural networks. The proposed method consists of three main steps. First, historical [...] Read more.
This paper proposes a novel method for the real-time prediction of photovoltaic (PV) power output by integrating phase space reconstruction (PSR), improved grey wolf optimization (GWO), and long short-term memory (LSTM) neural networks. The proposed method consists of three main steps. First, historical data are denoised and features are extracted using singular spectrum analysis (SSA) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Second, improved grey wolf optimization (GWO) is employed to optimize the key parameters of phase space reconstruction (PSR) and long short-term memory (LSTM) neural networks. Third, real-time predictions are made using LSTM neural networks, with dynamic updates of training data and model parameters. Experimental results demonstrate that the proposed method has significant advantages in both prediction accuracy and speed. Specifically, the proposed method achieves a mean absolute percentage error (MAPE) of 3.45%, significantly outperforming traditional machine learning models and other neural network-based approaches. Compared with seven alternative methods, our method improves prediction accuracy by 15% to 25% and computational speed by 20% to 30%. Additionally, the proposed method exhibits excellent prediction stability and adaptability, effectively handling the nonlinear and chaotic characteristics of PV power. Full article
(This article belongs to the Special Issue Control Design and Numerical Computation in Manufacturing Process System)
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19 pages, 14073 KiB  
Article
Research on the Real-Time Detection of Red Fruit Based on the You Only Look Once Algorithm
by Song Mei, Wenqin Ding and Jinpeng Wang
Processes 2024, 12(1), 15; https://doi.org/10.3390/pr12010015 - 20 Dec 2023
Viewed by 1047
Abstract
The real-time and accurate recognition of fruits and vegetables is crucial for the intelligent control of fruit and vegetable robots. In this research, apple picking is selected. This study proposed a lightweight, coupon-product, neural-net terminal YOLO algorithm for apple image recognition. Compared with [...] Read more.
The real-time and accurate recognition of fruits and vegetables is crucial for the intelligent control of fruit and vegetable robots. In this research, apple picking is selected. This study proposed a lightweight, coupon-product, neural-net terminal YOLO algorithm for apple image recognition. Compared with the YOLO series algorithm, the tiny algorithm shows a strong relationship with the calculation speed. In traditional red fruit detection, the recognition time is generally several seconds, which is unacceptable in the real-time system. In this research, a total of 2000 apple images from different environments are used as a dataset for training and testing. The YOLOv4-tiny model is detailed, instructed, and used for the identification. The indicators, such as F1Score (0.92) and mAP (95.5% average), are analyzed by calculating the loss rate, accuracy rate (96.21%), and recall rate (95.47%). Finally, the algorithm shows good accuracy and high speed (no more than 5 ms) in online real-time detection. Full article
(This article belongs to the Special Issue Control Design and Numerical Computation in Manufacturing Process System)
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19 pages, 3801 KiB  
Article
A Hybrid Evolutionary Algorithm for Multi-Stage Workshop Sequencing in Car Production
by Cuimei Chen, Jun Liu and Jia Liu
Processes 2023, 11(10), 2990; https://doi.org/10.3390/pr11102990 - 17 Oct 2023
Viewed by 1153
Abstract
During the car production process, diverse production workshops have distinct prerequisites for car body sequencing. This results in the intricate nature of sequencing within multi-stage car workshops. In this study, an optimization method for car body sequencing is proposed that combines a hybrid [...] Read more.
During the car production process, diverse production workshops have distinct prerequisites for car body sequencing. This results in the intricate nature of sequencing within multi-stage car workshops. In this study, an optimization method for car body sequencing is proposed that combines a hybrid evolutionary algorithm with heuristic rules. In the welding workshop, a genetic algorithm is employed to optimize the vehicle sequencing. Simultaneously, a differential evolution algorithm is used to optimize the inbound sequence of the buffer zone between the welding and painting workshops, as well as the inbound sequence of the buffer zone between the painting and assembly workshops. Heuristic rules are applied to optimize the outbound sequence of the buffer zone between the welding and painting workshops, as well as the outbound sequence of the buffer zone between the painting and assembly workshops. In addition, in order to improve the quality of the initial population, a heuristic method-based initial population construction method is proposed. The optimization objectives are the number of vehicle model changes in the welding workshop, the number of color changes in the painting workshop, and the total number of overloads in the assembly workshop. The experimental results show that the proposed method performs better than the five outstanding evolutionary algorithms. Full article
(This article belongs to the Special Issue Control Design and Numerical Computation in Manufacturing Process System)
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20 pages, 11943 KiB  
Article
Research on Data Collection Methods for Assembly Performance of Array Antennas in Digital Twin Workshops
by Xuepeng Guo, Linyan Liu, Zhexin Wang, Huifen Wang, Xiaodong Du, Jiancheng Shi and Yue Wang
Processes 2023, 11(9), 2711; https://doi.org/10.3390/pr11092711 - 11 Sep 2023
Cited by 1 | Viewed by 1290
Abstract
Aimed at the characteristics of multi-source heterogeneity and the rapid generation of data in digital twin workshops, as well as the current situation where communication protocols between equipment within the workshop vary greatly and are difficult to interconnect, a data integration method based [...] Read more.
Aimed at the characteristics of multi-source heterogeneity and the rapid generation of data in digital twin workshops, as well as the current situation where communication protocols between equipment within the workshop vary greatly and are difficult to interconnect, a data integration method based on OPC UA is designed. Firstly, combining the process flow and data source characteristics of array antenna assembly, a data collection and transmission scheme based on OPC UA was designed. Secondly, a process information model of array antenna assembly was established to realize data perception and transmission and solve the difficulties of complex data structure, high real-time requirements, and heterogeneous data in digital twin workshop. Finally, the proposed method and model were applied to the performance prediction platform for an array antenna assembly process based on digital twins, achieving perception of process data during the assembly process of array antennas, and achieving performance prediction and visualization for various stages of array antennas based on assembly process data. Full article
(This article belongs to the Special Issue Control Design and Numerical Computation in Manufacturing Process System)
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16 pages, 1825 KiB  
Article
The Evaluation Technology of Manufacturer Intelligence Regarding the Selection of the Decision Support System of Smart Manufacturing Technologies: Analysis of China–South Africa Relations
by Fengque Pei, Jiaxuan Zhang, Minghai Yuan, Fei He and Bingwen Yan
Processes 2023, 11(7), 2185; https://doi.org/10.3390/pr11072185 - 21 Jul 2023
Viewed by 1113
Abstract
With the development of international cooperation, South Africa (SA) has been China’s largest trading partner in Africa for several consecutive years. China and SA can build the digital “Belt and Road” to modernize the manufacturing system locally and optimize process control by benchmarking [...] Read more.
With the development of international cooperation, South Africa (SA) has been China’s largest trading partner in Africa for several consecutive years. China and SA can build the digital “Belt and Road” to modernize the manufacturing system locally and optimize process control by benchmarking with the best-in-class manufacturers in each country. In this research, an evaluation technology of manufacturer intelligence regarding the selection of decision support system (DSS) of smart manufacturing technologies, analyzing China–South Africa relations, is described. Firstly, the three keys aspects that enable the technologies of DSS are discussed in detail. Then, one key technology, the manufacturers’ intelligent evaluation system with 15 indexes, was built. The indexes and their measurements are also proposed. Finally, a fusion method based on boosting with multi-kernel function (online sequential extreme learning machine based on boosting, Boosting-OSELM) is introduced. The purpose of Boosting-OSKELM is to combine several weak learners into a strong learner (lower mean square error, MSE) through an acceptable time delay. Finally, the case study is presented to demonstrate the improvement on the MSE and process time, showing a relative MSE improvement of 96.19% and a relative time delay ratio of 31.46%. Totally, the largest contribution of the proposed evaluation method in this study is the conversion of the history data saved by the manual scoring method into knowledge in accessible MES and resealable time delay, which will free up the expert workforce in the entire process. We expect this paper will help future research in this field. Full article
(This article belongs to the Special Issue Control Design and Numerical Computation in Manufacturing Process System)
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16 pages, 4759 KiB  
Article
Prediction of Array Antenna Assembly Accuracy Based on Auto-Encoder and Boosting-OSKELM
by Yifei Tong, Miao Wang and Tong Zhou
Processes 2023, 11(5), 1460; https://doi.org/10.3390/pr11051460 - 11 May 2023
Viewed by 1116
Abstract
As a critical component for space exploration, navigation, and national defense, array antenna secures an indispensable position in national strategic significance. However, various parts and complex assembly processes make the array antenna hard to meet the assembly standard, which causes repeated rework and [...] Read more.
As a critical component for space exploration, navigation, and national defense, array antenna secures an indispensable position in national strategic significance. However, various parts and complex assembly processes make the array antenna hard to meet the assembly standard, which causes repeated rework and delay. To realize the accurate and efficient prediction of the assembly accuracy of array antenna, a prediction method based on an auto-encoder and online sequential kernel extreme learning machine with boosting (Boosting-OSKELM) is proposed in this paper. The method is mainly divided into two steps: Firstly, the auto-encoder with the fine-tuning trick is used for training and representation reduction of the data. Then, the data are taken as the input of Boosting-OSKELM to complete the initial training of the model. When new sample data is generated, Boosting-OSKELM can realize the online correction of the model through rapid iteration. Finally, the test shows that the average MSE of Boosting-OSKELM and ANN is 0.061 and 0.12, and the time consumption is 0.85 s and 15 s, respectively. It means that this method has strong robustness in prediction accuracy and online learning ability, which is conducive to the development of array antenna assembly. Full article
(This article belongs to the Special Issue Control Design and Numerical Computation in Manufacturing Process System)
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