Date of Award
3-2023
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Operational Sciences
First Advisor
Nathan B. Gaw, PhD
Abstract
Studies have shown a connection between early catastrophic engine failures with microtexture regions (MTRs) of a specific size and orientation on the titanium metal engine components. The MTRs can be identified through the use of Electron Backscatter Diffraction (EBSD) however doing so is costly and requires destruction of the metal component being tested. A new methodology of characterizing MTRs is needed to properly evaluate the reliability of engine components on live aircraft. The Air Force Research Lab Materials Directorate (AFRL/RX) proposed a solution of supplementing EBSD with two non-destructive modalities, Eddy Current Testing (ECT) and Scanning Acoustic Microscopy (SAM). Doing so will require registration of SAM and ECT which is not a simple task. This paper focuses on providing a proof of concept of performing automatic image registration using a Convolutional Neural Network (CNN) to register two titanium metal images from a scanning technique similar to EBSD known as Polarized Light Microscopy (PLM).
AFIT Designator
AFIT-ENS-MS-23-M-128
Recommended Citation
Johnston, Nathan A., "Automated Registration of Titanium Metal Imaging of Aircraft Components Using Deep Learning Techniques" (2023). Theses and Dissertations. 6997.
https://scholar.afit.edu/etd/6997
Comments
A 12-month embargo was observed.
Approved for public release. Case number on file.