Automated Image Registration for Titanium Aircraft Components via Resolution-Robust Parallel Neural Networks

Author

Paige T. Luebbering

Date of Award

3-2024

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Operational Sciences

First Advisor

Nathan B. Gaw, PhD

Abstract

Titanium alloys are vital to the structural integrity of military and commercial aircraft, comprising numerous critical components. These components are composed of microtexture regions (MTRs) that, at a specific size and orientation, can lead to aircraft failure. Existing MTR testing methods, such as Electron Backscatter Diffraction, often fall short in effectively detecting these MTRs without causing damage to the component. Addressing this gap, this thesis develops a Parallel Convolutional Neural Network (CNN) model tailored for multi-resolution image registration of Polarized Light Microscopy (PLM) images to enhance MTR identification in a non-invasive manner. The findings reveal a significant enhancement in the model’s ability to register images accurately at lower resolutions, with the F-statistic for the Mean Squared Error (MSE) regression indicating a strong predictive capability (p=0.0069), suggesting that the model’s performance significantly improves as the resolution diminishes. Furthermore, the model notably outperforms established methods such as Mutual Information and SIFT, with statistically significant differences in mean SAD values (p<0.0001), underscoring its potential for non-destructive evaluation techniques. These advancements in non-destructive evaluation techniques significantly enhance the operational readiness and safety of military and commercial aircraft by offering a more accurate, reliable, and non-invasive method for MTR identification.

AFIT Designator

AFIT-ENS-MS-24-M-087

Comments

A 12-month embargo was observed for posting this work on AFIT Scholar.

Distribution Statement A, Approved for Public Release. PA case number on file.

Recommended Citation

Luebbering, Paige T., "Automated Image Registration for Titanium Aircraft Components via Resolution-Robust Parallel Neural Networks" (2024). Theses and Dissertations. 7722.
https://scholar.afit.edu/etd/7722