Dr. Jianjun Shi
A member of National of Academy of Engineering
The Carolyn J. Stewart Chair and Professor
H. Milton Stewart School of Industrial and System Engineering
Georgia Institute of Technology
Wednesday, October 16, 2024
2:00-3:00 pm Seminar, 3:00-4:00 pm Reception
Location: CBIS Auditorium
A multistage manufacturing process (MMP) refers to a manufacturing system consisting of multiple machines, stations, or operations to finish a final product. The quality of the final product is a result of complex interactions among multiple stages. In other words, the quality characteristics of one stage are not only influenced by local variations at that stage but also by variations propagated from upstream stages. With the advancement of sensing and computing technologies, in-situ sensor outputs in MMP has been evolving, from univariate data to multivariate data, to functional curves, to images, to 3D point cloud data, and to high-resolution videos. At the same time, data science methods employed have also been evolving dramatically from the early time when PCA, clustering and classification, and state space models were developed, to the later time when Bayesian models and big data analysis were used, and to the most recent time when one sees the application of tensor-based models, Koopman operator theory, and numerous emerging machine learning methods. This talk will tell the story behind the research journey in the past 30 years for modeling and analysis of MMP. It will discuss the co-evolution of data science and data modality, and how their evolution drives and propels the advancement in modeling and analysis of MMP. This presentation provides a summary of major milestones in modeling and analysis of MMP, how it evolved with sensing data modality and data science, and where it was implemented. Some recent research on 3D point cloud data modeling and control in 3D/4D printing, as well as some ideas about future research directions will be discussed as well.
Dr. Jianjun Shi is the Carolyn J. Stewart Chair and Professor in School of Industrial and Systems Engineering, with a joint appointment in School of Mechanical Engineering, both at Georgia Institute of Technology. Dr. Shi is a member of National of Academy of Engineering (NAE) of the USA, an Academician of the International Academy for Quality (IAQ), a Fellow of five professional societies, including ASME, IISE, INFORMS, ISI, and SME. Dr. Shi’s research focuses on data enabled manufacturing, in-process quality improvement, system informatics and control. He received numerous recognitions, including the George Box Medal (2022), the ASQ Walter Shewhart Medal (2021), The S. M. Wu Research Implementation Award (2021), ASQ Brumbaugh Award (2019), The Horace Pops Medal Award (2018), IISE David F. Baker Distinguished Research Award (2016), the IIE Albert G. Holzman Distinguished Educator Award (2011), Forging Achievement Award from Forging Industry Educational and Research Foundation (2007), Monroe-Brown Foundation Research Excellence Award (2007), the 1938E Award (1998) at the University of Michigan, and NSF CAREER Award (1996). More information about Dr. Shi can be found at https://sites.gatech.edu/jianjun-shi/.
