Introduction
Aircraft Body Defect Detection
Aerospace Maintenance, Repair, and Operations (MRO) face significant challenges when it comes to the precise and efficient identification of defects, as well as ensuring timely maintenance procedures. These challenges can result in a cascade of issues, including potential delays, elevated operational costs, and heightened risks to the safety and overall reliability of aerospace systems.
Challenges
Challenges Encountered in Aircraft Defect Inspection.
Inaccuracies in Defect Identification
Aircraft and aerospace systems, with over 50 possible defects, pose challenges in detection, especially for hidden parts. Advanced tech and skilled personnel are vital for safety and reliability.
Limited Real-world Data Availability
Obtaining a comprehensive dataset of real-world aircraft surface defects can be very challenging and expensive. Most of the time, the defects may have only occured once, which makes it hard to train the defect recognition system effectively.
Scalability of Machine Learning Training
Defects frequently involve intricate and diverse scenarios from surface defects to potential hardware malfunctions, making it challenging to replicate them on actual aircraft materials. Moreover, this approach is cost-ineffective.
Sigmawave’s Solutions
Transforming aerospace maintenance, Sigmawave uses visual synthetic data to expedite aircraft defect detection:
Benefits
Unlocking Benefits in Military Applications with Sigmawave AI
