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.
Recommended Citation
Lake, Rami Issac, "AI-Based Defect Detection in Aerospace Ultrasonic Signals" (2024). Graduate Research Theses & Dissertations. 7902.
https://huskiecommons.lib.niu.edu/allgraduate-thesesdissertations/7902
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