Mechanical Engineering Seminar

Title:

An Overview of Recent Research Thrusts in Additive Manufacturing at Sandia National Laboratories

Speaker:

Dr. Jonathan Pegues

Affiliation:

Sandia National Laboratories

When:

Friday, October 28, 2022 at 2:00:00 PM   

Where:

GTMI Building, Room 114

Host:

Christopher Saldaña
christopher.saldana@me.gatech.edu
14043853735

Abstract

The design space offered by additive manufacturing (AM) has provided new and exciting pathways for material development and optimization to solve long standing design and performance challenges. Contrasting from conventional subtractive manufacturing methods, AM enables complex design and spatial composition and microstructural grading. The impact of these advanced manufacturing capabilities is currently being explored at Sandia National Laboratories (SNL) to provide a flexible and agile solution to meet evolving and demanding national security needs. This talk highlights several recent AM R&D thrusts at Sandia to advance both the technology and capabilities. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525


Biography

Jonathan Pegues received his PhD in Mechanical Engineering from Auburn University utilizing additive manufacturing to fabricate fatigue resistant stainless steels capable of outperforming their wrought counterparts. During this time, he also served as a project engineer with the National Center for Additive Manufacturing Excellence (NCAME), working on several projects related to establishing process-structure-property relationships for additive manufactured metallic materials. He joined Sandia National Laboratories as a postdoctoral appointee in 2019 with the Coatings and Additive Manufacturing group, supporting materials development of high entropy alloys and refractory metals. In 2020 he converted to staff to support qualification activities for additive manufacturing processes. His research interest in additive manufacturing center on advancing the technology to design around long-standing materials failure challenges by optimizing the complex process-structure-property relationships