Digital Twin Smart Manufacturing

Aims & Scope

The progression of technologies -- including artificial intelligence (AI), big data analytics, the Internet of Things (IoT), and complex system modeling -- has continually opened fresh opportunities while posing critical challenges for the manufacturing industry. Digital twin technology serves as a unifying methodological framework for the integrated deployment of these technologies, enabling seamless cyber-physical integration.


A focal point of discussion for both researchers and industry professionals is: How can the conceptual strengths of digital twin be  utilized to augment the deep cognitive capabilities of manufacturing systems, optimize production quality and throughput, and reduce operational costs?


Addressing this imperative, this session provides an open platform for interdisciplinary dialogue on innovative applications of digital twin in smart manufacturing.


The session topics include but are not limited to:

  • Digital Twin & Smart Manufacturing Equipment

  • Digital Twin & Smart Manufacturing Processes

  • Digital Twin & Smart Manufacturing Workshops


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For session presentation applications, please contact the session chairs below:

Tianliang Hu

Email: tlhu#sdu.edu.cn (replace # with @)

Giovanni Lugaresi

Email: giovanni.lugaresi#kuleuven.be(replace # with @)


Session Chairs

Presentations

  • Professor
    Harbin University of Science and Technology
    ‌Digital Twin Technology and Applications for Optimizing Machining Processes
    Abstract Coming soon
  • Professor
    Shandong University (China)
    Digital Twin based contour error prediction and compensation for machine tool and robotics
    Abstract Coming soon
  • Professor
    Guizhou University (China)
    Modeling and Intelligent Optimization of Manufacturing Processes Driven by Industrial Foundation Models and Digital Twins
    Abstract Coming soon
  • Professor
    Shanghai Jiaotong University (China)
    Digital Twin for Manufacturing Execution Management and Control of Discrete Manufacturing Systems
    Abstract Coming soon
  • Professor
    Nanhang University (China)
    Online inference of residual stress field in large-scale components based on deformation force monitoring data
    Abstract Coming soon
  • Associate Professor
    Dalian University of Technology (China)
    Shape-performance multi-feature sensing technology for the assembly process supports aero-engine digital twin
    Abstract Coming soon
  • Postdoctoral Research Fellow
    Harbin Institute of Technology (China)
    Digital Twin-Enabled Coupled Error Modeling and Predictive Analysis for Assembly Processes
    Abstract Coming soon
  • Assistant Professor
    KU Leuven (Belgium)
    Graph-model Based Online Calibration of Production System Digital Twins
    Abstract Coming soon
  • Ph. D Student
    Shandong University (China)
    Digital Twin Based Human-Machine Collaboration
    Abstract Coming soon