Application of Artificial Intelligence Models for Enhancing Non-Destructive Inspection of Aircraft Wheel Tie Bolt Component Through Fluorescent Penetrant Inspection

Abstract

Non-destructive testing (NDT) is crucial for maintaining the structural integrity of aircraft components, particularly wheel tie bolts that are prone to surface defects like cracks due to stress and environmental factors. Fluorescent Penetrant Inspection (FPI), with its high sensitivity compared to Dye Penetrant Testing (DPT), is commonly used in the aviation industry to detect such anomalies. This study presents an application of artificial intelligence (AI) image detection inspection method. FPI images of the aircraft wheel tie bolt of a narrow body type of aircraft were trained and analyzed. The defects were identified and inspected using machine learning. The findings present an application, which can improve the aerospace Non-Destructive testing procedures with higher detection consistency on human inspection.