SUBJECT: Ph.D. Dissertation Defense
BY: Myong Joon Kim
TIME: Friday, January 6, 2023, 10:00 a.m.
PLACE: MARC Building, 114
TITLE: Processing and Predictive Modeling of Thin-walled Geometries by Directed Energy Deposition
COMMITTEE: Christopher Saldana, Chair (ME)
Thomas Kurfess (ME)
Shreyes Melkote (ME)
V. Roshan Joseph (ISYE)
Wanhong Yang (Delta TechOps)


Blown powder directed energy deposition (DED) is a prominent additive manufacturing technique for near-net-shape part production and for repair manufacturing for the aerospace, automotive, and defense industrial sectors. Thin substrate deposition by DED, wherein the width of the substrate is smaller than that of the laser diameter, is a critical topic of interest for building and repairing complex components in various geometries and has potential to impact the production of precision parts with minimum post-processing. Despite widespread interest and research on DED, a rigorous understanding of process-structure response in deposition and post-processing of thin-walled geometry has yet to be developed. The proposed study will address this gap by focusing on three main objectives: (1) understanding dependent relationships between deposition quality (e.g., defect structure, grain structure, geometry, surface roughness), substrate geometry and process variables, (2) exploring efficacy of post-processing strategies for mitigating internal defects and external surface quality by laser remelting, and (3) developing and validating a multi-fidelity prediction model to relate deposition and post-processing parameters to part quality outcomes. Collectively, the proposed framework and fundamental knowledge generated will advance predictive modelling for process optimization efforts, further the scientific understanding of thin substrate deposition in conjunction with defect removal strategies and provide robust tools for further implementation.