Material Synthesis and Machine Learning for Additive Manufacturing
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.
Subject Area
Materials science|Computer science|Mechanical engineering|Artificial intelligence
Recommended Citation
Regis, Jaime Eduardo, "Material Synthesis and Machine Learning for Additive Manufacturing" (2022). ETD Collection for University of Texas, El Paso. AAI29207800.
https://scholarworks.utep.edu/dissertations/AAI29207800