DIGITAL TWIN FOR AIRCRAFT ENGINE NACELLE INSPECTION

The project successfully demonstrates an automated crack detection and monitoring system for aircraft engine nacelles using image processing and digital twin concepts. The system detects surface cracks through its pixel-based analysis method which measures crack severity and displays crack development through its graphical and dashboard presentation system. The results demonstrate that crack growth stages correspond to different severity levels which confirms that the method works effectively. The system improves inspection operations by delivering dependable inspection results which help maintenance teams make better decisions while decreasing their need for manual inspections. The proposed solution increases aviation safety and reliability while advancing the development of smart structural health monitoring systems. The system requires future development through the integration of advanced AI models which trained specifically for crack detection because this technology will enhance system performance through its accurate detection capabilities. The inclusion of real-time sensors and IoT-based data acquisition can enable continuous monitoring instead of periodic inspection. The system will gain real-world applicability through its extension which enables users to perform 3D crack analysis and subsurface defect detection. The development of a cloud-based platform can allow remote monitoring of multiple aircraft, and the approach can be expanded to other structural components such as wings and fuselage. The system will become a complete predictive maintenance solution which organizations can use through these advancements.

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