ORCID

https://orcid.org/0000-0003-3639-3881

Date of Award

Spring 5-15-2020

Author's School

McKelvey School of Engineering

Author's Department

Computer Science & Engineering

Degree Name

Master of Science (MS)

Degree Type

Thesis

Abstract

Many industries are rapidly adopting additive manufacturing (AM) because of the added versatility this technology offers over traditional manufacturing techniques. But with AM, there comes a unique set of security challenges that must be addressed. In particular, the issue of part verification is critically important given the growing reliance of safety-critical systems on 3D printed parts. In this thesis, the current state of part verification technologies will be examined in the con- text of AM-specific geometric-modification attacks, and an automated tool for 3D printed part verification will be presented. This work will cover: 1) the impacts of malicious attacks on AM using geometrically-modified 3D models, 2) a 3D part reconstruction approach from medical imaging scans, 3) a mesh alignment technique based on point set registration, de- signed to handle abnormal part geometries, and 4) an automatic error detection and defect visualization tool for comparing the geometric similarity of 3D printed parts to their intended geometries.

Language

English (en)

Chair

Ning Zhang

Committee Members

Ning Zhang Steve Cole Chiamaka Asinugo

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