Aircraft components, such as lavatory parts, are subject to rigorous quality inspections to ensure safety and aesthetics. Traditionally, inspectors conduct detailed visual checks, often taking substantial time and relying on experience to detect defects. Recently, Safran has begun integrating artificial intelligence (AI) into their inspection workflows, significantly reducing inspection times and improving defect detection accuracy.
Using AI systems developed by companies like Loopr AI, Safran generates synthetic data that mimics real defects, enhancing training datasets where actual examples are scarce. These tools analyze images of parts—such as toilet lids and cabin components—to identify issues like scratches, paint bubbles, and discoloration. The AI models have demonstrated a recall rate of over 90%, enabling quicker and more consistent inspection outcomes and allowing for increased production throughput.
Flexible Inspection Methods
The AI-based inspection approaches employed by Safran vary, including fully automated systems and hybrid setups that combine human oversight with machine analysis. For example, inspection of painted surfaces benefits from automated image capture and analysis, with human inspectors reviewing findings for validation. This synergy ensures high accuracy while maintaining flexibility in diverse inspection scenarios.
With these advancements, Safran reports a 10-15% increase in production capacity, as the AI inspection process routinely accelerates defect detection and documentation. The company is expanding the deployment of these technologies across its facilities, illustrating how AI is transforming quality assurance in the aviation manufacturing sector.
