Advanced sensing technologies provide high-volume data for additive manufacturing (AM) quality improvement. Various engineering knowledge, such as kinematics information and physical process models, can be incorporated to guide the statistical learning for enhanced modeling accuracy and interpretability. In this talk, the presenter will introduce examples of the ongoing research of her lab on the integration of process knowledge into statistical modeling, monitoring, and process authentication for AM processes. Real-world case studies are used to demonstrate the effectiveness of the proposed approaches. Finally, several challenges and research opportunities are discussed for AM quality control in cyber-physical systems.
Dr. Wenmeng Tian received her Ph.D. degree in Industrial and Systems Engineering from Virginia Tech in 2017. She is currently an assistant professor from the Department of Industrial and Systems Engineering at Mississippi State University. Her research focuses on advanced sensing and analytics for advanced manufacturing process modeling, monitoring, and prognosis, which has been applied to both subtractive and additive manufacturing processes. Her publications have appeared in journals such as IISE Transactions, Journal of Manufacturing Science and Engineering, and Additive Manufacturing. Her work has been funded by NSF, DoD, DoL, and industrial institutes. She recently received the NSF CAREER Award in 2021.
