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Digital Twins for Smart Manufacturing

Distinguished Research Seminar Series

20251014Yuebin GuoWang Chunjin Event Image
  • Date

    14 Oct 2025

  • Organiser

    Department of Industrial and Systems Engineering, PolyU

  • Time

    10:00 - 11:30

  • Venue

    Online via ZOOM  

Speaker

Prof. Yuebin Guo

Remarks

Meeting link will be sent to successful registrants

20251014Yuebin GuoWang Chunjin Poster

Summary

Digital twins are rapidly emerging as core enablers of smart manufacturing, offering cyber–physical integration through real-time synchronization of virtual models and physical entities. A digital twin leverages IoT sensing, modeling, and bidirectional data flow to capture manufacturing system dynamics with high fidelity, supporting real-time prediction, prescriptive decision-making, and adaptive control across the manufacturing value chain, leading to higher productivity, reduced downtime, and resilient operations. This seminar will examine the digital twin concept and architecture. We will discuss enabling technologies such as IoT, AI-driven modeling, model synchronization with live data, model predictive control, and edge–cloud continuum that support scalable deployment. The seminar will also present case studies in subtractive and additive manufacturing. Finally, the seminar will discuss future challenges in heterogeneous data integration, real-time model updating, uncertainty quantification, sensing-learning-control latency, and cross-domain orchestration for intelligent digital twins.

Keynote Speaker

Prof. Yuebin Guo

Prof. Yuebin Guo

Professor
Department of Mechanical and Aerospace Engineering, Rutgers University-New Brunswick, USA

Prof. Yuebin Guo is Henry Rutgers Distinguished Professor and Director of New Jersey Advanced Manufacturing Institute at Rutgers University-New Brunswick, USA. Prior to Rutgers, he served as the Assistant Director for Research Partnerships at the U.S. Advanced Manufacturing National Program Office. He was also an Alexander von Humboldt Fellow at RWTH Aachen and Fraunhofer IPT, Germany. His research focuses on manufacturing processes, scientific machine learning, AI-driven digital twins, and surface integrity. He has published more than 300 peer-reviewed technical publications in these areas. He is a recipient of numerous awards, including the ASME William T. Ennor Manufacturing Technology Award, the SME Albert M. Sargent Progress Award, the Rutgers Board of Trustees Award for Excellence in Research, and the Alexander von Humboldt Research Award. He is an elected fellow of the American Society of Mechanical Engineers (ASME), the Society of Manufacturing Engineers (SME), and the International Academy for Production Engineering (CIRP).

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