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Physics-Based Sensing and Machine Learning for Smart Manufacturing

Distinguished Research Seminar Series

20220301Robert X GaoEvent Banne
  • Date

    01 Mar 2022

  • Organiser

    Department of Industrial and Systems Engineering, PolyU

  • Time

    10:00 - 11:20

  • Venue

    Online via ZOOM  


Prof. Robert X. Gao


Meeting link will be sent to successful registrants

20220301Robert X GaoPoster


As the fundamental building blocks of Industry 4.0, sensing and artificial intelligence play a critical role in advancing the state-of-manufacturing. The ability in acquiring data in-situ and extracting clues from the data to guide the action of assistive infrastructure such as robots is essential to enhancing process control and production planning.

This seminar highlights research on manufacturing process-embedded sensing and ma-chine learning for smart manufacturing, illustrated in two examples. The first example de-scribes the design and experimental evaluation of a multi-physics sensor with acoustic-based wireless data transmission capability for the online quantification of melt temperature, pressure, velocity, and viscosity within an injection mold. The second example illustrates machine learn-ing methods for the recognition of current actions and prediction of future actions of human operators during assembly operations, which provide the prerequisites for human-robot collab-orative assembly. The presentation highlights the potential of use-inspired basic research in advancing the state of manufacturing.

Keynote Speaker

Prof. Robert X. Gao

Prof. Robert X. Gao

Cady Staley Professor of Engineering
Chair, Department of Mechanical and Aerospace Engineering
Case Western Reserve University, US


Robert Gao is the Cady Staley Professor of Engineering and Department Chair of Mechanical and Aerospace Engineering at Case Western Reserve University in Cleveland, Ohio. Since receiving his Ph.D. degree from the Technical University of Berlin, Germany in 1991, he has been working on signal transduction methods, stochastic modeling, and machine learning for improving the observability of dynamical systems such as manufacturing equipment and processes, with the goal to improve process and product quality control.  Prof. Gao is a Fellow of the IEEE, ASME, SME, and CIRP (International Academy for Production Engineering). He has published more than 400 technical papers, including more than 180 journal articles, three books, and holds 13 patents. He is a recipient of the SME Eli Whitney Productivity Award, ASME Blackall Machine Tool and Gage Award, IEEE Instrumentation and Measurement Society’s Technical Award, IEEE Best Application in Instrumental and Measurement Award, and NSF CAREER award. He is a Senior Editor for the IEEE/ASME Transactions on Mechatronics.    

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