Date of Award
Summer 2018
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Electrical & Computer Engineering
Committee Director
Jiang Li
Committee Member
Dimitrie C. Popescu
Committee Member
Masha Sosonkina
Committee Member
William P. Winfree
Abstract
The Nondestructive Evaluation Sciences Branch (NESB) at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) has conducted impact damage experiments over the past few years with the goal of understanding structural defects in composite materials. The Data Science Team within the NASA LaRC Office of the Chief Information Officer (OCIO) has been working with the Non-Destructive Evaluation (NDE) subject matter experts (SMEs), Dr. Cheryl Rose, from the Structural Mechanics & Concepts Branch and Dr. William Winfree, from the Research Directorate, to develop computer vision solutions using digital image processing and machine learning techniques that can help identify the structural defects in composite materials.
The research focused on developing an autonomous Non-Destructive Evaluation system which detects, identifies, and characterizes crack and delamination in composite materials from computed tomography (CT scans) images. The identification and visualization of cracking and delamination will allow researchers to use volumetric models to better understand the propagation of damage in materials, leading to design optimizations that will prevent catastrophic failure.
Rights
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
DOI
10.25777/vc78-t122
ISBN
9780438538252
Recommended Citation
Delelegn, Desalegn T..
"Non-Destructive Evaluation for Composite Material"
(2018). Master of Science (MS), Thesis, Electrical & Computer Engineering, Old Dominion University, DOI: 10.25777/vc78-t122
https://digitalcommons.odu.edu/ece_etds/37
ORCID
0000-0002-1149-7011
Included in
Artificial Intelligence and Robotics Commons, Electrical and Computer Engineering Commons, Materials Science and Engineering Commons