Speaker：Jing Zou, Data Scientist, ZhenDui Industry Artificial Intelligence Co. Ltd
Time：9:00 - 10:00, December 7, 2023 (Beijing Time)
Location：200, Yue-Kong Pao Library's Annex
Abstract：
Advance manufacturing systems are becoming increasing complex, subjecting to constant changes driven by market demands, new technology insertion, as well as random disruption events. To support daily operation, distributed sensors are utilized to monitor the status of each process. While information about production processes has been becoming increasing transparent, detailed, and real-time, the utilization of this information for real-time manufacturing analysis and decision-making has been lagging behind largely due to the limitation of the traditional methodologies for production system analysis, and a lack of real-time data-driven manufacturing processes modeling, analysis and control approaches. Hence, it is necessary to develop manufacturing system real-time data-driven modeling, analysis and control approaches for improving system responsiveness and efficiency.
In this presentation, we will introduce a novel manufacturing system real-time data-driven mathematical model to describe the system production dynamics. Furthermore, systematic methods will be illustrated to identify the causes of system permanent production loss. In the end, real-time production control policies based on the system diagnostic results will be presented to improve system profit, energy efficiency and responsiveness.
Biography：
JING ZOU received the B.S. degree from the Huazhong University of Science and Technology, Wuhan, China, in 2012, the M.S. degree from the University of Florida, Gainesville, FL, USA, in 2014, and the Ph.D. degree from Stony Brook University, Stony Brook, NY, USA, in 2019, all in mechanical engineering.
He is currently a data scientist with ZDIAI. His current research interests include smart manufacturing, knowledge-basedAI, and data-driven manufacturing system modeling and analysis.
