Author ORCID Identifier

https://orcid.org/0009-0006-9152-0977

Date Available

7-31-2024

Year of Publication

2024

Document Type

Doctoral Dissertation

Degree Name

Doctor of Philosophy (PhD)

College

Engineering

Department/School/Program

Chemical and Materials Engineering

Advisor

Dr. Paul F. Rottmann

Abstract

The adoption of additive manufacturing across a wide range of fields is ever-increasing, allowing for solutions that were previously impossible to achieve. This dissertation demonstrates the wide range of macro- and micro- characterization techniques used to investigate several precipitation strengthened AM alloy systems. While AM allows for nearly unlimited design freedom, this brings along its own issues as the interplay between scan strategy and part geometry is less well known. In this work, a novel thin-wall dogbone geometry was utilized to study one type of complex geometry observed in a large portion of AM focused designs. The mechanical behavior of these samples was collected through a custom micro-tensile load frame utilizing digital image correlation to calculate the full-field strain behavior. This allowed for detailed comparisons to samples extracted from larger AM parts as well as standard as-built dogbones. A detailed exploration of the microstructure was carried out using numerous electron beam and x-ray based techniques to elucidate differences caused by the varied thermal histories experienced during printing. The results of this work when combined with previously published solidification data for each alloy allowed for a much more cohesive understanding of the processing-property relationship in complex geometric areas within AM.

Digital Object Identifier (DOI)

https://doi.org/10.13023/etd.2024.306

Funding Information

This work was funded through a GAANN fellowship, internship with University Space Research Association, and a NASA KY Grant. This work was performed in part at the U.K. Electron Microscopy Center, a member of the National Nanotechnology Coordinated Infrastructure (NNCI), which is supported by the National Science Foundation (NNCI-2025075).

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