Date of Award

2022-05-01

Degree Name

Doctor of Philosophy

Department

Mechanical Engineering

Advisor(s)

Yirong Lin

Abstract

The goal of this research was to address three key challenges in additive manufacturing (AM), the need for feedstock material, minimal end-use fabrication from lack of functionality in commercially available materials, and the need for qualification and property prediction in printed structures. The near ultraviolet-light assisted green reduction of graphene oxide through L-ascorbic acid was studied with to address the issue of low part strength in additively manufactured parts by providing a functional filler that can strengthen the polymer matrix. The synthesis of self-healing epoxy vitrimers was done to adapt high strength materials with recyclable properties for compatibility with AM technology. Lastly, machine vision and machine learning were used for the autonomous characterization of micro and macrostructure and performance prediction in syntactic foams and lattice structures.

Language

en

Provenance

Recieved from ProQuest

File Size

79 p.

File Format

application/pdf

Rights Holder

Jaime Eduardo Regis

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