Hongyue Sun, Ph.D., Assistant Professor, Department of Industrial and Systems Engineering, University at Buffalo,Manufacturing Data Analytics with Functional Variables,3:00-4:00pm, 4th July, 2018,RM510, Shunde Building 2018.06.21

【Title】Manufacturing Data Analytics with Functional Variables
【Speaker】Hongyue Sun, Ph.D., Assistant Professor, Department of Industrial and Systems Engineering, University at Buffalo
【Host】Dr. Kaibo Wang
【Time】3:00-4:00pm, 4th July, 2018
【Venue】RM510, Shunde Building

【Abstract】Various manufacturing data are collected during product design, process operation, product quality assessment, etc. because of the advancement of sensing and information technology.  Among these data, functional variables can represent the in situ process conditions and rich product performance information, and are widely encountered in various manufacturing processes. These data provide great opportunities for real-time, proactive quality modeling and assurance. However, due to the lack of methods for analyzing heterogeneous data types including the functional data, the transformation of data to information and knowledge is still a challenging problem. In this talk, I will introduce several new data analytics methods for functional variables in advanced manufacturing, including functional hierarchical variable selection, functional graphical models, and interpretable functional feature selection. The above methods have been applied to many advanced manufacturing processes, such as additive manufacturing, aero-engine surface coating processes, crystal growth processes, printed electronics.

【Bio】Hongyue Sun is an assistant professor at the department of Industrial and Systems Engineering at the University at Buffalo. He received Bachelor’s degree in Mechanical Engineering and Automation from Beijing Institute of Technology, China, in 2012, M.S. degree in Statistics from Virginia Tech in 2015, and Ph.D. degree in Industrial Engineering from Virginia Tech in 2017. Dr. Sun’s research interests focus on 1) data analytics for advanced manufacturing quality modeling, monitoring, and control, and 2) data fusion for energy system knowledge transfer and scale-up. He is a member of ASME, IEEE, IISE, and INFOMRS. He can be reached at hongyues@buffalo.edu

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