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Intelligent Production and Manufacturing Systems
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Intelligent Production and Manufacturing Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 11117

Special Issue Editors

Dr. Sibao Wang

E-Mail Website
Guest Editor
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
Interests: intelligent manufacturing; high-performance machining; process planning
Dr. Ning Liu

E-Mail Website
Guest Editor
Digital Supply Chain, Advanced Remanufacturing and Technology Centre, Agency for Science, Technology and Research (A*STAR), Singapore 637143, Singapore
Interests: supply chain management; sustainable manufacturing; intelligent manufacturing; advanced manufacturing
Dr. Kai Ren

E-Mail Website
Guest Editor
1. State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
2. Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: additive manufacturing; process planning; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite submissions to the Special Issue on Intelligent Production and Manufacturing Systems.

Intelligent manufacturing integrates new-generation information technologies (Digital twin, deep learning, and so on) and advanced manufacturing technologies. It has been the trend in manufacturing technologies. Presently, a huge number of researchers have been dedicated to this area. Therefore, “Intelligent manufacturing and manufacturing system”, has been proposed and aims to cover the recent advances and future perspectives related to intelligent manufacturing, including manufacturing systems, supply chains, process planning and scheduling in the machining process, and additive manufacturing. The Special Issue welcomes outstanding research papers and review articles devoted to innovative methods for advanced modeling, planning, optimization, control, and monitoring technologies with the help of new-generation information technologies in the field of intelligent manufacturing and manufacturing systems.

In particular, the topics of interest include but are not limited to:

  • Intelligent manufacturing;
  • Digital twin in manufacturing;
  • Supply chain;
  • Manufacturing system;
  • Intelligent tool design;
  • Process planning;
  • Process optimization;
  • Production scheduling;
  • Modeling of the machining process;
  • Additive manufacturing.

Dr. Sibao Wang
Dr. Ning Liu
Dr. Kai Ren
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. Applied Sciences 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

  • intelligent manufacturing
  • supply chain
  • digital twin
  • sustainable manufacturing
  • manufacturing process modelling and optimization

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Published Papers (5 papers)

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Research

25 pages, 5516 KiB  
Article
Research on Flexible Job Shop Scheduling Problem with Handling and Setup Time Based on Improved Discrete Particle Swarm Algorithm
by Jili Kong and Zhen Wang
Appl. Sci. 2024, 14(6), 2586; https://doi.org/10.3390/app14062586 - 20 Mar 2024
Cited by 6 | Viewed by 2426
Abstract
With the gradual emergence of customized manufacturing, intelligent manufacturing systems have experienced widespread adoption, leading to a surge in research interests in the associated problem of intelligent scheduling. In this paper, we study the flexible job shop scheduling problem (FJSP) with setup time, [...] Read more.
With the gradual emergence of customized manufacturing, intelligent manufacturing systems have experienced widespread adoption, leading to a surge in research interests in the associated problem of intelligent scheduling. In this paper, we study the flexible job shop scheduling problem (FJSP) with setup time, handling time, and processing time in a multi-equipment work center production environment oriented toward smart manufacturing and make-to-order requirements. A mathematical model with the optimization objectives of minimizing the maximum completion time, the total number of machine adjustments, the total number of workpieces handled and the total load of the machine is constructed, and an improved discrete particle swarm algorithm based on Pareto optimization and a nonlinear adaptive inertia weighting strategy is proposed to solve the model. By integrating the model characteristics and algorithm features, a hybrid initialization method is designed to generate a higher-quality initialized population. Next, three cross-variance operators are used to implement particle position updates to maintain information sharing among particles. Then, the performance effectiveness of this algorithm is verified by testing and analyzing 15 FJSP test instances. Finally, the feasibility and effectiveness of the designed algorithm for solving multi-objective FJSPs are verified by designing an FJSP test example that includes processing time, setup time and handling time. Full article
(This article belongs to the Special Issue Intelligent Production and Manufacturing Systems)
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16 pages, 2863 KiB  
Article
An Improved Cuckoo Search Algorithm under Bottleneck-Degree-Based Search Guidance for Large-Scale Inter-Cell Scheduling Optimization
by Peixuan Yang, Qiong Liu and Shuping Xiong
Appl. Sci. 2024, 14(3), 1011; https://doi.org/10.3390/app14031011 - 24 Jan 2024
Viewed by 1275
Abstract
In order to deal with problems of reduced searching efficiency and poor quality of algorithms for large-scale inter-cell scheduling problems, an improved cuckoo search algorithm under bottleneck-degree-based search guidance is proposed. A large-scale inter-cell scheduling optimization model aiming at minimizing makespan is established. [...] Read more.
In order to deal with problems of reduced searching efficiency and poor quality of algorithms for large-scale inter-cell scheduling problems, an improved cuckoo search algorithm under bottleneck-degree-based search guidance is proposed. A large-scale inter-cell scheduling optimization model aiming at minimizing makespan is established. A tabu search is adopted to replace the local search strategy of the cuckoo search algorithm. The bottleneck degree of a complex network model for an inter-cell scheduling problem is used to guide the design of the neighborhood structure of the tabu search. The proposed algorithm is validated by numerical examples. The results show that the convergent speed and qualities of solutions of the proposed algorithm are improved. It is verified that the proposed search guidance based on a complex network’s bottleneck degree could improve the searching ability and convergence speed of the algorithm for large-scale inter-cell scheduling optimization problems. Full article
(This article belongs to the Special Issue Intelligent Production and Manufacturing Systems)
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20 pages, 4834 KiB  
Article
PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems
by Qingyun Yu, Bowen Jiang, Yaxuan Zhang, Wei Gong and Li Li
Appl. Sci. 2023, 13(20), 11439; https://doi.org/10.3390/app132011439 - 18 Oct 2023
Cited by 2 | Viewed by 1799
Abstract
There are currently many studies on data-driven optimization scheduling, but only a few studies have combined “closed-loop optimization” with “performance-driven”. Therefore, this research proposed a PSO-SVM-based (particle swarm optimization optimized support vector machine) scheduling method that reconciles the composite dispatching rules (CDR), performance-driving [...] Read more.
There are currently many studies on data-driven optimization scheduling, but only a few studies have combined “closed-loop optimization” with “performance-driven”. Therefore, this research proposed a PSO-SVM-based (particle swarm optimization optimized support vector machine) scheduling method that reconciles the composite dispatching rules (CDR), performance-driving ideology, and feedback mechanism ideology. Firstly, the composite dispatching rules coalesce flexible equipment maintenance, multiple process constraints, and dynamic dispatching. Secondly, the performance-driving ideology is carried out through two learning models based on the PSO-SVM algorithm, based on targeted optimizing performances. Thirdly, the feedback mechanism ideology makes the scheduling method realize closed-loop optimizations adaptively. Finally, the superiority of the proposed scheduling method is validated in a semiconductor manufacturing system in China. Compared with CDR, the proposed scheduling method combines MOV, PR, and EU, respectively EU_ O, EU_ P, PCSR and ODR increased by 7.85%, 5.11%, 8.76%, 8.14%, 6.60%, and 7.33%, indicating the superiority of this method. Full article
(This article belongs to the Special Issue Intelligent Production and Manufacturing Systems)
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32 pages, 17439 KiB  
Article
Modeling of Digital Twin Workshop in Planning via a Graph Neural Network: The Case of an Ocean Engineering Manufacturing Intelligent Workshop
by Jinghua Li, Wenhao Yin, Boxin Yang, Li Chen, Ruipu Dong, Yidong Chen and Hanchen Yang
Appl. Sci. 2023, 13(18), 10134; https://doi.org/10.3390/app131810134 - 8 Sep 2023
Cited by 4 | Viewed by 3172
Abstract
In the era of Industry 4.0 to 5.0, the manufacturing industry is dedicated to improving its production efficiency, control capability and competitiveness with intelligent enhancement. As a typical discrete manufacturing industry, it is difficult for ocean engineering (OE) manufacturers to accurately control the [...] Read more.
In the era of Industry 4.0 to 5.0, the manufacturing industry is dedicated to improving its production efficiency, control capability and competitiveness with intelligent enhancement. As a typical discrete manufacturing industry, it is difficult for ocean engineering (OE) manufacturers to accurately control the entire production process, and the establishment of an integrated system supported by digital twin (DT) technology is a better solution. This paper proposes a comprehensive set of system architectures for the DT workshop. It focuses on planning, which is the main line of control, to establish a model based on graph neural networks (GNNs) and suggests five decision-support approaches associated with the model from a practical application perspective. The utilization of complete twin data for prediction and visual simulation effectively eliminates the problem of unexpected factors interfering with scheduling in enterprise production planning and achieves the goals of rapid processing and just-in-time completion. The planning model is based on the attention mechanism, which characterizes the disjunctive graph, extracts the input GNN, and outputs the scheduling decision by constructing the multi-attention network of operations and machines to deal with the complicated “operation–machine” combination relationship. The proposed method has been verified in the case of structural assembly and welding workshops, has validity and reliability, and is superior to the traditional priority scheduling rules and heuristics in terms of precision rate and rapidity. Furthermore, the DT system completes the production line application, and its proven reliability supports its full-scale application in future smart factories. Full article
(This article belongs to the Special Issue Intelligent Production and Manufacturing Systems)
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13 pages, 10830 KiB  
Article
Ultra-Precision Cutting Mechanism of KDP Crystal in Microplastic Region via Heating Assistance
by Hong Yang, Siyuan Fu, Ming Huang, Zhonghao Cao, Baorui Wang, Guangwei Yang and Zhong Jiang
Appl. Sci. 2023, 13(12), 6865; https://doi.org/10.3390/app13126865 - 6 Jun 2023
Cited by 2 | Viewed by 1679
Abstract
The application range of potassium dihydrogen phosphate (KDP) crystals can be expanded by enhancing their surface quality properties. Therefore, a method for controlling the surface-temperature field of various materials was developed to expand the plastic zone to overcome the difficulty in processing KDP [...] Read more.
The application range of potassium dihydrogen phosphate (KDP) crystals can be expanded by enhancing their surface quality properties. Therefore, a method for controlling the surface-temperature field of various materials was developed to expand the plastic zone to overcome the difficulty in processing KDP crystals. The ductile/brittle transition depth of the KDP crystals was determined using a 38 nm nanoindentation experiment. The nanoscratch experiment revealed the rules of how the transformation depth of the KDP crystals changes with temperatures, and the effect of temperature on the microstructure of the KDP crystals was studied. Finally, KDP crystal surfaces were processed using a UPDFC machine at elevated temperatures. According to our experiments, the surface roughness of the KDP crystal reached 5.275 nm as temperature increased, thus enhancing its surface quality. This method could be applied to other brittle materials. Full article
(This article belongs to the Special Issue Intelligent Production and Manufacturing Systems)
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