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ECE Seminar Series — May 1 (Fri) @ 2:00pm: "Digital Twins for Inspection of Direct Ink Write Additive Manufacturing," Brian Giera, Director, Data Science Institute, LLNL

Date and Time
photo of brian giera

Location: Harold Frank Hall, Room 4108 (ECE Conf. Room)
Come at 1:30p for Cookies, Coffee and Conversation
DISTINGUISHED LECTURE at the ECE SEMINAR SERIES

Abstract

Inspection presents the largest bottleneck in the National Nuclear Security Administration's (NNSA) manufacturing enterprise and is as much as 4x more costly than fabrication itself. On top of this, there are no known methods to connect machine instructions to the final performance of a part. Our work addresses these limitations using process- and part-scale digital twins: a framework that combines in situ process monitoring, data-driven prediction enhancement, virtual inspection, and virtual reality (VR) visualization. This holistic approach aims to alleviate the bottleneck of part certification for critical applications across the NNSA mission space. While we are using the Direct Ink Write process as the initial demonstrator, the broader goal is to instantiate a digital twin framework that applies to other advanced processes of programmatic interest, including advanced conventional manufacturing methods.

This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, LLNL-ABS-856841.

Bio

Brian Giera joined Lawrence Livermore National Laboratory in 2014 as a postdoctoral researcher and is the principal investigator of an LDRD Strategic Initiative on digital twins for virtual inspection of advance manufacturing as well as technical lead on several advanced analytics and additive manufacturing projects. Giera’s research interests include additive manufacturing, digital twins, machine learning, in situ process monitoring, computational materials science, molecular dynamics, electrophoretic deposition and hyperspectral imaging—all with a focus on developing and applying physics-based and machine learning models to a variety of advanced manufacturing systems. Giera holds a B.S. in Chemical Engineering from Purdue University and a Ph.D. in Chemical Engineering from the University of California, Santa Barbara.

Hosted by: Distinguished Lecture at the ECE Seminar Series and UCSB RealAI

Submitted by: Adele Myers Lantow <adele@umail.ucsb.edu>