时间：2026年4月28日(周二)10:00-12:00
地点：李兆基科技大楼A402会议室
报告人：Xiayun Zhao
邀请人：林峰 教授
报告人简介：
Dr. Zhao is a tenured Associate Professor in the Department of Mechanical Engineering and Materials Science at the University of Pittsburgh. She earned her B.S. from the Department of Precision Instruments in Tsinghua University (Beijing), and M.S. and Ph.D. in Mechanical Engineering from Georgia Institute of Technology (Atlanta). She also has prior industry experience as an instrumentation and control systems engineer in the oil and gas sector. Dr. Zhao leads the ZIP-AM (ZXY Intelligent Precision – Advanced Manufacturing) Laboratory, where her research advances measurement science, sensing, and control technologies for additive manufacturing systems. Her work integrates precision engineering with cyber-physical systems to enable intelligent, data-driven manufacturing. She is the recipient of the U.S. National Science Foundation CAREER Award (2023). As a lead principal investigator, she has secured funding from the U.S. Department of Energy, the National Science Foundation (including CAREER and Future Manufacturing programs), and the Manufacturing Pennsylvania Innovation Program, with additional project support from NSF Industry–University Cooperative Research Centers. Her research spans metal additive manufacturing and photopolymerization-based 3D printing, conducted in collaboration with interdisciplinary teams. More information about Dr. Zhao’s research can be found at her laboratory website.
报告摘要：
Additive manufacturing (AM) enables the fabrication of complex, high-performance components, but achieving consistent quality requires deeper integration of artificial intelligence (AI) with process monitoring and control. In situ multimodal sensing provides critical insight into dynamic process behavior, forming the foundation for data-driven modeling and intelligent manufacturing.
This talk presents multimodal sensing approaches for laser powder bed fusion (LPBF) and vat photopolymerization (VPP), enabled by in-house developed sensing technologies. For LPBF, we deploy a patented coaxial two-wavelength imaging pyrometer, together with a high-speed off-axis camera and a lab-designed fringe projection profilometry system, to quantify melt pool dynamics, spatter behavior, and layer-wise surface features. For VPP, we introduce lab-developed interferometric and ultrasonic sensing techniques to estimate layer thickness, degree of cure, and elastic modulus in situ.
Experimental results demonstrate how sensor fusion and machine learning enable process–structure–property modeling and property prediction. This work highlights a pathway toward intelligent AM through real-time, operando characterization for improved process control and reliability.
