Impact Score 5.94
Industrial manufacturing enterprises around the world are experiencing the transformation from pure electromechanical enterprises to Mechatronics enterprises, finally entering into digital-driven enterprises, which also lays the foundation of building digital twins. The digital twins is a specific application technology of CPS (Cyber-Physical Systems). It inherits the concept of digitization, visualization, modeling and localization of objective physical objects in digital factory, and also gives the integration of computing process and physical process in CPS concept. Based on the network of environment perception and physical equipment, resources, information, objects and people are closely linked. The framework of the digital twins is closely around “human, information model and physical object”. It is the industrial Internet of things (IoT) that can realize the information bidirectional interaction among all elements. The popularization and application of new information technologies, such as IoT and big data, digital twins, 3D visualization and big data technology, provide the condition for realizing the innovation and development of products and the optimization of global resources allocation.
The framework of digital twins includes the digital twins method and digital twins model. In different stages of the whole life cycle of the manufacturing factory, the focus of attention is different, and the methods and models of digital twins construction will be different accordingly. However, at any stage, digital twins offers a specific application technology framework for the manufacturing industry to realize the CPS concept, which brings breakthrough to the implementation of industry 4.0 strategy. Therefore, the digital twins is a leading technology with a guiding significance for the realization of intelligent manufacturing. The concept of digital twins will affect the future manufacturing system. The potential value of digital twins starts to be considered from the aspects of design, engineering, operation and maintenance, and simulation will be the hot topic to be studied. The modern industrial production line is characterized by high automation, a real-time communication network and an advanced communication interface. Meanwhile, new material technology and chip technology greatly reduce the cost of data acquisition means like RFID, which provides grounds for the popularization of real-time data acquisition, helping to obtain a large number of industrial IoT data.
Based on mixing production reality and virtual technology, a closed-loop production system is built, that is, the digital model of product life cycle management should play a role in the real production process, and every step of production execution will generate data feedback. The application of digital twins technology makes the value of data increasingly prominent. Big data are used for analysis in almost all fields, and the extensive data assets, known as a new production factor, must be used more properly. This special issue welcomes the contributions from the scholars in the fields of industrial design, data mining, IoT, etc., and jointly discusses the leading technologies of industrial manufacturing that digital twins create.
Suggested topics include:
Application of digital twins in virtual environment industrial products production
Modeling and Simulation in production and assembly process
The whole process management of industrial design based on the digital twins
Data integration of advanced industrial production value chain
Application of digital twins in industry 3D visualization platform
Data mining and decision-making in industrial production
Design and transformation process optimization based on the digital twins
Remote supervision of digital twins system in industrial production and manufacturing
Industrial automation process based on the digital twins
"Digital twins + Internet of things"- based intelligent industrial manufacturing
Predictive maintenance mechanism of the digital twins in industrial manufacturing
Realization of three-dimensional operation and maintenance of advanced industrial production based on the digital twins
Dynamic scheduling of intelligent workshop by the digital twins
Application of digital twins in spinning intelligent factory
Application of the digital twin in multi-dimensional intelligent manufacturing space
New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this issue, the authors agree to referee one paper (if asked) within the time frame of the special issue.
Before submission, authors should carefully read the Guide for Authors available at
Authors should submit their papers through the journal's web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI-twin” under the “Issues” tab.
For additional questions, contact the Main Guest Editor.
Submission of manuscript: December 20, 2021
First notification: April 20, 2022
Submission of revised manuscript: May 20, 2022
Notification of the re-review: June 20, 2022
Final notification: July 6, 2022
Final paper due: August 31, 2022
Publication: April 20, 2022
Professor, North China Sea Offshore Engineering Survey Institute, Ministry Of Natural Resources North Sea Bureau, Qingdao, China
Email: [email protected]
Google Scholar: https://scholar.google.com/citations?hl=en&user=DHFmsXQAAAAJ
ORCID ID: https://orcid.org/0000-0003-1059-4765
Haibin Lv is a Professor at the North China Sea Offshore Engineering Survey Institute, Ministry Of Natural Resources North Sea Bureau, Qingdao, China. He received PhD. Degree from the First Institute of Oceanography, State Oceanic Administration at 1990. He has 30 years research and practical experience in Geoinformatics, Image Processing, Marine Surveying and Mapping, Evaluation and Assessment of the Marine Environment. He has completed dozens of consulting and research projects in related fields. He has won the Chinese National Oceanic Science and Technology Progress Award for two times. He has published more than 80 high quality papers. He is member of Editor Board of Plos one, IET Image Processing. He is Leading Guest Editor of IEEE TITS. He is reviewer of IEEE TII, IEEE IOTJ, IEEE Access, Multimedia Tools and Applications, Plos One, Scientific Reports.
Associate Professor, Burgundy University, France
Email: [email protected]
Google Scholar: https://scholar.google.fr/citations?user=c3EPy9sAAAAJ&hl=fr
Bouziane BRIK received engineering degree in computer science, ranked first in his class, and the Magister degree from the University of Laghouat, Algeria, in 2010 and 2013, respectively, and the Ph.D. degree from Laghouat and La Rochelle (France) universities, in 2017. He is currently working as associate professor at Burgundy (Bourgogne) university and DRIVE laboratory. Before joining Burgundy university, he was a post-doc at university of Troyes, CESI school, and Eurecom school. He has been working on network slicing in the context of H2020 European projects on 5G including MonB5G and 5GDrones projects. His research interests also include the Internet of Things (IoT), the IoT in industrial systems, smart grid, and vehicular networks. He also acted or still acts as a Reviewer of many IFIP, ACM, and IEEE conferences (ICC, Globecom, PIMRC, WCNC, VTC, IM/NOMS, IWCMC, GIIS, WiMob, and Wireless Days) and journals such as the IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, the IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (ITS), the IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, the IEEE Communication Magazine, and the IEEE Network Magazine.
Associate Professor, School of Computing and Information Technology, University of Wollongong, Australia
Email: [email protected]
Google Scholar: https://scholar.google.com/citations?user=Bf6gvGkAAAAJ&hl=en
Jun Shen was awarded Ph.D. in 2001 at Southeast University, China. He held positions at Swinburne University of Technology in Melbourne and University of South Australia in Adelaide before 2006. He is an Associate Professor in School of Computing and Information Technology at University of Wollongong in Wollongong, NSW of Australia, where he had been Head of Postgraduate Studies, and Chair of School Research Committee since 2014. He is a senior member of three institutions: IEEE, ACM and ACS. He has published more than 230 papers in journals and conferences in CS/IT areas. His expertise includes computational intelligence, bioinformatics, cloud computing and learning technologies including MOOC. He has been (Associate) Editor, PC Chair, Guest Editor, PC Member for numerous journals and conferences published by IEEE, ACM, Elsevier and Springer. A/Prof Shen was also member of ACM/AIS Task Force on Curriculum MSIS 2016. His publications appeared at IEEE Transactions on Learning Technologies, IEEE Transactions on ITS, IEEE Transactions on Services Computing, Briefs in Bioinformatics, British Journal of Education Technologies, International Journal of Information Management and many others.