Implementation of Machine Learning in Aircraft Maintenance
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
UMT, Lahore
Abstract
The study investigates the application of machine learning techniques
for detecting surface defects on aircraft metal and composite surfaces. The
methodology involved selecting, training, and testing using YOLO V9, on
datasets containing both general and aircraft-specific defects. YOLO was
chosen for its real-time processing capabilities and high accuracy. The
results showed significant improvements in defect detection accuracy and
efficiency, with the final model achieving high precision for cracks,
scratches, and corrosion. The development of an intuitive user interface
further ensures accessibility for maintenance personnel, highlighting the
potential of integrating advanced machine learning techniques into aircraft
maintenance to improve inspection processes.