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Machines
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Digital Twin Applications in Smart Manufacturing
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Digital Twin Applications in Smart Manufacturing

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Advanced Manufacturing".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 44170

Special Issue Editors

Prof. Dr. Jiafu Wan

grade E-Mail Website
Guest Editor
School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou 510641, China
Interests: cyber-physical systems; intelligent manufacturing; digital twin; big data analytics; industry 4.0; smart factory
Dr. Yuqian Lu

E-Mail Website
Guest Editor
Department of Mechanical Engineering, The University of Auckland, Private Bag 92019, Auckland 1142, New Zealand
Interests: smart manufacturing; intelligent robotics; machine learning; manufacturing control
Prof. Dr. Sang Do Noh

E-Mail Website
Guest Editor
Department of Systems Management Engineering, Sungkyunkwan University, Suwon-si 16417, Republic of Korea
Interests: manufacturing systems; modeling and simulation; CAD/CAM/PLM/digital manufacturing; smart manufacturing; cyber physical system; digital twin
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital twin (DT) has recently attracted much attention worldwide as a newly emerging and fast-growing technology. The product is the core of smart manufacturing, and the improvement of product processing, manufacturing mode and product quality are related to the success of the transformation and upgrading of the whole manufacturing industry. In traditional smart manufacturing, the integration and management of physical space data and information space data in the manufacturing process are lacking, the data are fragmented and, thus, closed-loop feedback cannot be achieved, and it is impossible to establish an accurate intelligent control system model. How to apply DT theory and technology to better serve the smart manufacturing of products has become a key issue that needs to be urgently solved.

Smart manufacturing is a typical complex system which is very suitable for observation and research using DT theory. DT covers the whole life cycle and value chain of products, and establishes digital archives from raw materials, design, process, manufacturing, use and maintenance, which lays a data foundation for the whole process of quality traceability and continuous improvement of product research and development. Meanwhile, the deep combination of emerging technologies (such as artificial intelligence, big data, Internet of Things, etc.) and DT further promotes the real-time interaction and integration of information space and physical space, playing an increasingly important role in product manufacturing, and has become the cornerstone of smart manufacturing. Thus, it is expected that primary and emerging research topics about bringing

More specifically, this Special Issue “Digital Twin Applications in Smart Manufacturing” will cover intelligent perception and information fusion, intelligent modeling and analysis, intelligent collaborative optimization control, intelligent analysis and optimization decision-making, and precise execution and service for DT in smart manufacturing. This Special Issue will focus on (but not be limited to) the following topics:

  • Reference architecture of DT in smart manufacturing
  • High-performance intelligent perception and comprehensive cognition of manufacturing data for DT
  • Dynamic intelligent modeling with deep integration of mechanism model and data model in DT
  • Implementation, operation, and optimization of DT in smart manufacturing
  • Multi-layer decision and optimal control method of end-edge-cloud based on DT
  • New smart manufacturing scheduling using DT
  • DT and big data-driven preventive maintenance in smart manufacturing
  • DT-based dynamic adjustment and control service
  • Distributed DT system and its applications in smart manufacturing

Prof. Dr. Jiafu Wan
Dr. Yuqian Lu
Prof. Dr. Sang Do Noh
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. Machines 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

  • Smart manufacturing
  • Internet of Things
  • Digital twin technology
  • Design and operation, and optimization
  • Manufacturing big data
  • Industrial Artificial Intelligence
  • Multiple disciplines
  • Multi-objective optimization

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Published Papers (10 papers)

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Research

19 pages, 3675 KiB  
Article
A Novel Method of Digital Twin-Based Manufacturing Process State Modeling and Incremental Anomaly Detection
by Qinglei Zhang, Zhen Liu, Jianguo Duan and Jiyun Qin
Machines 2023, 11(2), 151; https://doi.org/10.3390/machines11020151 - 22 Jan 2023
Cited by 5 | Viewed by 2393
Abstract
In the manufacturing process, digital twin technology can provide real-time mapping, prediction, and optimization of the physical manufacturing process in the information world. In order to realize the complete expression and accurate identification of and changes in the real-time state of the manufacturing [...] Read more.
In the manufacturing process, digital twin technology can provide real-time mapping, prediction, and optimization of the physical manufacturing process in the information world. In order to realize the complete expression and accurate identification of and changes in the real-time state of the manufacturing process, a digital twin framework of incremental learning driven by stream data is proposed. Additionally, a novel method of stream data-driven equipment operation state modeling and incremental anomaly detection is proposed based on the digital twin. Firstly, a hierarchical finite state machine (HFSM) for the manufacturing process was proposed to completely express the manufacturing process state. Secondly, the incremental learning detection method driven by stream data was used to detect the anomaly of the job process data, so as to change the job status in real time. Furthermore, the F1 value and time consumption of the proposed algorithm were compared and analyzed using a general dataset. Finally, the method was applied to the practical case development of a welding manufacturer’s digital twin system. The flexibility of the proposed model is calculated by the quantitative method. The results show that the proposed state modeling and anomaly detection method can help the system realize job state mapping and state change quickly, effectively, and flexibly. Full article
(This article belongs to the Special Issue Digital Twin Applications in Smart Manufacturing)
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18 pages, 3916 KiB  
Article
Digital Twin-Based Analysis and Optimization for Design and Planning of Production Lines
by Donggun Lee, Chong-Keun Kim, Jinho Yang, Kang-Yeon Cho, Jonghwan Choi, Sang-Do Noh and Seunghoon Nam
Machines 2022, 10(12), 1147; https://doi.org/10.3390/machines10121147 - 1 Dec 2022
Cited by 11 | Viewed by 3961
Abstract
With the increasing dynamic nature of customer demand, production, product, and manufacturing design changes have become more frequent. Moreover, inadequate validation during the manufacturing design phase may result in additional issues, such as process redesign and layout reallocation, during the operation phase. Therefore, [...] Read more.
With the increasing dynamic nature of customer demand, production, product, and manufacturing design changes have become more frequent. Moreover, inadequate validation during the manufacturing design phase may result in additional issues, such as process redesign and layout reallocation, during the operation phase. Therefore, systems that can pre-validate and allow accurate and reliable analysis in the manufacturing design phase, as well as apply and optimize variations in production lines in real time, are required. Previously, digital twin (DT) has been studied a lot in product design and facility prognostics and management fields. Research on the system framework leading to DT utilization and optimization and analysis through DT in complex manufacturing systems with continuous processes such as production lines is insufficient. In this study, a system based on a DT and simulation results is developed; this system can reflect, analyze, and optimize dynamic changes in the design of processes and production lines in real time. First, the framework and application of the proposed system are designed. Subsequently, optimization methodologies based on heuristics and reinforcement learning (RL) are developed. Finally, the effectiveness and applicability of the proposed system are verified by implementing an actual DT application at a real manufacturing site. Full article
(This article belongs to the Special Issue Digital Twin Applications in Smart Manufacturing)
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15 pages, 3056 KiB  
Article
SWLC-DT: An Architecture for Ship Whole Life Cycle Digital Twin Based on Vertical–Horizontal Design
by Wei Xiao, Ming He, Zhengxian Wei and Nianbin Wang
Machines 2022, 10(11), 998; https://doi.org/10.3390/machines10110998 - 30 Oct 2022
Cited by 6 | Viewed by 2836
Abstract
With the development of IoT technology, the digital twin has been applied in many fields. It is the key to realizing the integration of physical information space and an effective means for intelligent upgrading of products, providing a novel idea for the whole [...] Read more.
With the development of IoT technology, the digital twin has been applied in many fields. It is the key to realizing the integration of physical information space and an effective means for intelligent upgrading of products, providing a novel idea for the whole life cycle management of complex products. As a pillar industry at the national strategic level, the shipbuilding industry is in the stage of informatization transformation and upgrading and needs to improve its own competitiveness. The ship whole life cycle includes design, construction, operation, and maintenance, as well as scrapping and recycling, but each stage has a certain independence, which makes it prone to the problem of information islands. However, the current research on the product full lifecycle digital twin has not yet considered the impact of historical data of successive generation products on each stage of the current product lifecycle. To address the above issues, this paper firstly proposes the vertical–horizontal design idea from the perspective of the product whole life cycle and combining historical experience (vertical) with real-time data (horizontal) to realize the construction and evolution of digital twin models at all stages of the life cycle. Then, on the basis of the vertical–horizontal design idea, a framework for the ship whole life cycle digital twin is proposed. Finally, the operation mechanism of the framework is elaborated from the four stages of the ship life cycle, with a view to providing a reference for the transformation and upgrading of the future ship industry. Full article
(This article belongs to the Special Issue Digital Twin Applications in Smart Manufacturing)
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15 pages, 1954 KiB  
Article
Integrated Smart Warehouse and Manufacturing Management with Demand Forecasting in Small-Scale Cyclical Industries
by Yuk-Ming Tang, George To Sum Ho, Yui-Yip Lau and Shuk-Ying Tsui
Machines 2022, 10(6), 472; https://doi.org/10.3390/machines10060472 - 14 Jun 2022
Cited by 16 | Viewed by 6450
Abstract
In the context of the global economic slowdown, demand forecasting, and inventory and production management have long been important topics to the industries. With the support of smart warehouses, big data analytics, and optimization algorithms, enterprises can achieve economies of scale, and balance [...] Read more.
In the context of the global economic slowdown, demand forecasting, and inventory and production management have long been important topics to the industries. With the support of smart warehouses, big data analytics, and optimization algorithms, enterprises can achieve economies of scale, and balance supply and demand. Smart warehouse and manufacturing management is considered the culmination of recently advanced technologies. It is important to enhance the scalability and extendibility of the industry. Despite many researchers having developed frameworks for smart warehouse and manufacturing management for various fields, most of these models are mainly focused on the logistics of the product and are not generalized to tackle the specific manufacturing problem facing in the cyclical industry. Indeed, the cyclical industry has a key problem: the big risk which high sensitivity poses to the business cycle and economic recession, which is difficult to foresee. Despite many inventory optimization approaches being proposed to optimize the inventory level in the warehouse and facilitate production management, the demand forecasting technique is seldom focused on the cyclic industry. On the other hand, management approaches are usually based on the complex logistics process instead of integrating the inventory level of the stock, which is very crucial to composing smart warehouses and manufacturing. This research study proposed a digital twin framework by integrating the smart warehouse and manufacturing with the roulette genetic algorithm for demand forecasting in the cyclical industry. We also demonstrate how this algorithm is practically implemented for forecasting the demand, sustaining manufacturing optimization, and achieving inventory optimization. We adopted a small-scale textile company case study to demonstrate the proposed digital framework in the warehouse and demonstrate the results of demand forecasting and inventory optimization. Various scenarios were conducted to simulate the results for the digital twin. The proposed digital twin framework and results help manufacturers and logistics companies to improve inventory management. This study has important theoretical and practical significance for the management of the cyclical industry. Full article
(This article belongs to the Special Issue Digital Twin Applications in Smart Manufacturing)
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15 pages, 1934 KiB  
Article
Digital-Twins-Driven Semi-Physical Simulation for Testing and Evaluation of Industrial Software in a Smart Manufacturing System
by Keqiang Cheng, Qiang Wang, Dongyu Yang, Qingyun Dai and Meilin Wang
Machines 2022, 10(5), 388; https://doi.org/10.3390/machines10050388 - 18 May 2022
Cited by 14 | Viewed by 3570
Abstract
To satisfy the needs of the individualized manufacturing of products, the smart manufacturing system (SMS) is frequently reconfigured. To quickly verify the reliability and adaptability of industrial software in reconfiguring the SMS for new or upgraded product orders, a semi-physical simulation method for [...] Read more.
To satisfy the needs of the individualized manufacturing of products, the smart manufacturing system (SMS) is frequently reconfigured. To quickly verify the reliability and adaptability of industrial software in reconfiguring the SMS for new or upgraded product orders, a semi-physical simulation method for testing and evaluation of industrial software is proposed based on digital-twins-driven technology. By establishing a semi-physical simulation model of SMS, the reliability and robustness of the software system are quickly verified by running industrial software in various manufacturing scenarios. In this paper, the key technologies to carry out semi-physical simulation testing and evaluation of industrial software for SMSs are expounded in detail, including how to synchronize cyber and physical systems, how to conduct semi-physical accelerated simulation testing, and how to identify defects quickly in industrial software used in actual production environments. By establishing a semi-physical simulation production line model for stepper motors, the effectiveness and practicality of the proposed approach are verified, and the testing verification time of industrial software is significantly reduced. Finally, the robustness of the industrial software for SMS is further verified by conducting fault injection testing, so as to provide implications for fault prognostics or fault-prevention research. Full article
(This article belongs to the Special Issue Digital Twin Applications in Smart Manufacturing)
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22 pages, 3334 KiB  
Article
Digital Twins-Based Production Line Design and Simulation Optimization of Large-Scale Mobile Phone Assembly Workshop
by Rongli Zhao, Guangxin Zou, Qianyi Su, Shangwen Zou, Wenshun Deng, Ailin Yu and Hao Zhang
Machines 2022, 10(5), 367; https://doi.org/10.3390/machines10050367 - 11 May 2022
Cited by 12 | Viewed by 5108
Abstract
The mobile phone is a typical 3C electronic product characterized by frequent replacement, multiple product specifications, high flexibility, high-frequency production line switching, and urgent delivery time during production. Therefore, the optimized design of the mobile phone production workshop is crucial. This paper takes [...] Read more.
The mobile phone is a typical 3C electronic product characterized by frequent replacement, multiple product specifications, high flexibility, high-frequency production line switching, and urgent delivery time during production. Therefore, the optimized design of the mobile phone production workshop is crucial. This paper takes the assembly process of a specific type of mobile phone assembly as the research object and adopts the heuristic balance method to combine the production procedures. Moreover, it considers the automation degree of the process and the demand for production line rhythm to carry out station division and working hours design for the assembly process. The advantages and disadvantages of the plug-and-play production line and unit production line architecture are integrated, aiming at the production line’s construction cost and unit area capacity. A hybrid workshop with a mixed combination of two types of production lines is designed and an optimization model of hybrid workshop design is established. The semi-physical simulation technology of digital twins is utilized to verify the proposed design scheme to achieve the balance optimization of the production line, improve production efficiency, and reduce production costs. This work provides a technical scheme for designing and optimizing large-scale mobile phone assembly workshops with multi-batch and high-frequency production changes. Full article
(This article belongs to the Special Issue Digital Twin Applications in Smart Manufacturing)
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27 pages, 2998 KiB  
Article
Integration of Design, Manufacturing, and Service Based on Digital Twin to Realize Intelligent Manufacturing
by Luyao Zhang, Lijie Feng, Jinfeng Wang and Kuo-Yi Lin
Machines 2022, 10(4), 275; https://doi.org/10.3390/machines10040275 - 12 Apr 2022
Cited by 14 | Viewed by 4468
Abstract
Complex product design, manufacturing, and service are the key elements of a product’s life cycle. However, the traditional manufacturing processes of design, manufacturing, and service are independent of each other, so lack deep integration. The emergence of digital twins offers an opportunity to [...] Read more.
Complex product design, manufacturing, and service are the key elements of a product’s life cycle. However, the traditional manufacturing processes of design, manufacturing, and service are independent of each other, so lack deep integration. The emergence of digital twins offers an opportunity to accelerate the integration of complex product design, manufacturing, and services. For intelligent manufacturing, physical entity and virtual entity transformation can be realized through digital information. A collaborative framework for complex product design, manufacturing, and service integration based on digital twin technology was proposed. The solutions of process integration, data flow, modeling and simulation, and information fusion were analyzed. The core characteristics and key technologies of service-oriented manufacturing, design for service and manufacturing, and manufacturing monitoring based on the deep integration of the digital twin were discussed. Finally, the feasibility of the framework was verified by a self-balancing multistage pump manufacturing case. The performance of the upgraded pump under the framework was tested, and the test results proved the effectiveness of the integrated framework. Full article
(This article belongs to the Special Issue Digital Twin Applications in Smart Manufacturing)
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29 pages, 7348 KiB  
Article
Digital Twin-Based Integrated Assessment of Flexible and Reconfigurable Automotive Part Production Lines
by Jinho Yang, Yoo Ho Son, Donggun Lee and Sang Do Noh
Machines 2022, 10(2), 75; https://doi.org/10.3390/machines10020075 - 21 Jan 2022
Cited by 14 | Viewed by 3973
Abstract
The manufacturing industry has witnessed rapid changes, including unpredictable product demand, diverse customer requirements, and increased pressure to launch new products. To deal with such changes, the reconfigurable manufacturing system has been proposed as one of the advanced manufacturing systems that is close [...] Read more.
The manufacturing industry has witnessed rapid changes, including unpredictable product demand, diverse customer requirements, and increased pressure to launch new products. To deal with such changes, the reconfigurable manufacturing system has been proposed as one of the advanced manufacturing systems that is close to the realisation of smart manufacturing since it is able to reconfigure its hardware, software, and system structures in a much quicker manner. Conventional simulation technologies lack convergence with physical manufacturing systems, and reconfigurable manufacturing lines require the manual construction of production line models for each reconfiguration. This study presents a digital twin-based integrated reconfiguration assessment application that synchronises with real-time manufacturing data and provides accurate, automated simulation functionality to build and analyse a manufacturing system. The paper discusses the architectural design and implementation of the application, an information model, and an assessment model that enable quantitatively assessment on reconfigurations of manufacturing systems from various aspects. The effectiveness of the proposed application is verified via application to an automotive parts production line to assess the reconfiguration indicators of the manufacturing system under different scenarios. The results reveal that the proposed application provides faster and more accurate reconfiguration assessments compared to existing methods. The findings of this study are expected to facilitate accurate and consistent decision making for evaluating the various indicators of production line performance. Full article
(This article belongs to the Special Issue Digital Twin Applications in Smart Manufacturing)
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19 pages, 2707 KiB  
Article
A Hierarchical Integrated Modeling Method for the Digital Twin of Mechanical Products
by Menglei Zheng and Ling Tian
Machines 2022, 10(1), 2; https://doi.org/10.3390/machines10010002 - 21 Dec 2021
Cited by 14 | Viewed by 4342
Abstract
With the development of information and communication technology, massive amounts of data are generated during the entire lifecycle of mechanical products. However, their isolated and fragmented state hinders further empowerment of smart manufacturing. Digital twins have attracted considerable attention as they enable a [...] Read more.
With the development of information and communication technology, massive amounts of data are generated during the entire lifecycle of mechanical products. However, their isolated and fragmented state hinders further empowerment of smart manufacturing. Digital twins have attracted considerable attention as they enable a user to rebuild all elements of a physical entity in a virtual space, targeted at the effective fusion of data from multiple sources with different formats, while its modeling method still needs further research. In this context, we propose a native, full-element digital twin modeling method for mechanical products. This ontology-based method establishes a unified and computer-understandable model framework for mechanical products by abstracting the essential content and relationships of data and by storing them in a graph database efficiently. The developed model could serve as a data center for the entire lifecycle of the product or could be combined with existing data management systems, integrating the previously isolated, fragmented, and scattered data on various platforms. In addition, the model utilizes the structural characteristics of mechanical products and is developed as a hierarchical digital mapping to better meet the application requirements. Finally, a case study of a helicopter digital twin is presented to verify the proposed method. Full article
(This article belongs to the Special Issue Digital Twin Applications in Smart Manufacturing)
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19 pages, 54903 KiB  
Article
A Digital Twin-Oriented Lightweight Approach for 3D Assemblies
by Luo Fang, Qiang Liu and Ding Zhang
Machines 2021, 9(10), 231; https://doi.org/10.3390/machines9100231 - 9 Oct 2021
Cited by 10 | Viewed by 3460
Abstract
In the design and operation scenarios driven by Digital Twins, large computer-aided design (CAD) models of production line equipment can limit the real-time performance and fidelity of the interaction between digital and physical entities. Digital CAD models often consist of combined parts with [...] Read more.
In the design and operation scenarios driven by Digital Twins, large computer-aided design (CAD) models of production line equipment can limit the real-time performance and fidelity of the interaction between digital and physical entities. Digital CAD models often consist of combined parts with characteristics of discrete folded corner planes. CAD models simplified to a lower resolution by current mainstream mesh simplification algorithms might suffer from significant feature loss and mesh breakage, and the interfaces between the different parts cannot be well identified and simplified. A lightweight approach for common CAD assembly models of Digital Twins is proposed. Based on quadric error metrics, constraints of discrete folded corner plane characteristics of Digital Twin CAD models are added. The triangular regularity in the neighborhood of the contraction target vertices is used as the penalty function, and edge contraction is performed based on the cost. Finally, a segmentation algorithm is employed to identify and remove the interfaces between the two CAD assembly models. The proposed approach is verified through common stereoscopic warehouse, robot base, and shelf models. In addition, a scenario of a smart phone production line is applied. The experimental results indicate that the geometric error of the simplified mesh is reduced, the frame rate is improved, and the integrity of the geometric features and triangular facets is effectively preserved. Full article
(This article belongs to the Special Issue Digital Twin Applications in Smart Manufacturing)
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