Seminar: Holistic Modeling and Analysis of Manufacturing Systems with Applications in Additive Manufacturing and Occupational Safety, April 1

Attend the next IE Decision Systems Engineering Spring ’22 Seminar Series event hosted by School of Computing and Augmented Intelligence Associate Professor Rong Pan that explores applications in inkjet printing additive manufacturing and occupational safety.

Holistic Modeling and Analysis of Manufacturing Systems with Applications in Additive Manufacturing and Occupational Safety
Presented by Hongyue Sun, University of Buffalo

Friday, April 1, 2022
Noon MST
Artisan Court (BYAC) 270, Tempe campus [map] and Zoom


Recently, the increased digitization in manufacturing and Industry 4.0 have shown promise of better manufacturing systems description and decision-making. However, there is a lack of holistic analysis of manufacturing systems to incorporate the various information from process physics knowledge, high-dimensional sensing data, operator conditions and more.

In this talk, Hongyue Sun will present his group’s recent work with applications in an inkjet printing additive manufacturing process and occupational safety. In particular, the team addressed the process challenges in inkjet printing stages (i.e., jetting, evolution and solidification) based on streaming strobing videos and physics simulation to achieve process monitoring and modeling. First, an online anomaly detection method monitors the streaming jetting videos via online tensor decomposition and Bayesian change detection. Second, an integrated Graph Convolutional Network and Recurrent Neural Network captures the cross-linked, spatial and temporal relationships in co-evolving droplets under diverse material properties and process settings. As a result, they can not only forecast the future process dynamics in a known setting but also predict unexplored material/process settings. Third, an efficient emulation framework with joint tensor factorization and Nearest Neighbor Gaussian Process, or NNGP, emulates the high-dimensional physical model output of the solidification stage. Sun will also share work on improving the occupational safety of the warehouse and electric line operators based on wearable sensor analytics.

About the speaker

Hongyue Sun is an assistant professor at the Department of Industrial and Systems Engineering at the University at Buffalo. He received a Bachelor of Engineering degree in mechanical engineering from Beijing Institute of Technology, a Master of Science degree in statistics and a doctoral degree in industrial engineering from Virginia Tech, respectively.

Sun’s research interests are data analytics for advanced manufacturing, occupational safety and energy systems. His research has been recognized by a broad range of research communities in Quality Control and Reliability Engineering, Quality, Statistics, Reliability and Data Mining, including several best paper awards and finalists from INFORMS and IISE. His research has been funded by NSF, NIOSH and industry. He is a member of IISE, INFORMS, IEEE and ASME.