Wednesday, February 7th, 2024. Time: 3 p.m.

ABSTRACT

Luis Javier Segura, Zebin Li, Chi Zhou, Hongyue Sun

Droplet morphology and behavior substantially determine the quality of the Material Jetting (MJ) printed parts. However, obtaining consistent and stable droplet morphology and behavior is difficult because the droplets are very sensitive to different material and process parameters. This work investigates the droplet evolution prediction via Tensor Time Series (TTS) analysis. The cross-linked (i.e., underlying relationships shared across droplet evolution behaviors with diverse material and process parameters) and spatial-temporal relationships of the TTS are captured via Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural Network (TRNN), respectively. The method is tested in experimental and simulated droplet evolution data in the MJ process.

Keywords: Material Jetting, Additive Manufacturing, Droplet Evolution, Tensor Time Series, Deep Learning

SPEAKER CV

Luis Segura is an Assistant Professor in the Department of Industrial Engineering at the University of Louisville. He received a Ph.D. degree in Industrial and Systems Engineering from University at Buffalo in 2022. He also received a B.Sc. in Mechanical Engineering from Universidad de las Fuerzas Armadas-ESPE, Ecuador, in 2007; a M.Sc. in Manufacturing Systems from Southern Illinois University Carbondale, USA, as 2010 Fulbright Scholar. In 2013, he worked in the automotive systems design program at the Technical University of Eindhoven (TUe), Netherlands. From 2014 to 2018, he was a faculty member of the Mechanical Engineering Department at ESPE. His research interests lie in the integration of physics-based and data-driven models to optimize processes and product quality in advanced manufacturing. His research has been supported by Kentucky NSF EPSCoR and Jon Rieger Seed Grant. He received a NSF travel award for the MSEC 2019 and 2021, Fulbright Scholarship in 2010, first place UB ISE 2020 poster competition, honorable mention UB ISE researcher of the year in 2020, and a presidential fellowship. His teaching interest include statistical quality control, data analytics, and manufacturing systems. He is member of INFORMS, IIE, and ASME.