Publication Date

2024

Document Type

Dissertation/Thesis

First Advisor

Alhoori, Hamed

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Computer Science

Abstract

Ensuring the safety and integrity of materials and structures throughout the manufacturing cycle is a critical concern across various industries, including aerospace, automotive, oiland gas, and civil engineering. Non-Destructive Inspection (NDI) techniques allow for the examination of materials without causing damage or alteration, enabling the early detection of potential issues before materials are utilized in the field. The inspection of fuselage composites presents a particular challenge due to their complex structures, diverse materials, and differences in thickness, making defect detection a challenging yet crucial task. Moreover, defects of various types and causes can emerge across all depths of the material and at any stage of the manufacturing process, compounding the challenge. Furthermore, the process of manually inspecting and characterizing manufacturing flaws is time-intensive. With continued pressure to meet manufacturing output goals and increasing costs for skilled labor, there is a pressing need to develop NDI methods that not only increase production efficiency but also reduce production expenses. This work addresses these challenges by developing machine-learning models to assist inspectors in identifying defects more effectively. We employ multiple preprocessing methods to capture different characteristics of the ultrasonic signal across both standardized calibration data and real fuselage datasets.

Extent

72 pages

Language

en

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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