AI USE CASE
Airframe Structural Defect Detection AI
Automatically detect structural defects in airframes using computer vision on NDT inspection data.
What it is
This use case applies computer vision and machine learning to non-destructive testing (NDT) data, ultrasonic scans, X-ray images, thermography, to automatically identify cracks, corrosion, and delamination in airframe structures. It supports MRO teams in prioritising repair actions and generating audit-ready documentation, reducing aircraft-on-ground time and compliance risk.
Data you need
Historical NDT inspection datasets (ultrasonic, X-ray, or thermographic images) with labeled defect annotations, linked to airframe component records and maintenance history.
Required systems
- erp
- data warehouse
Why it works
- Build a curated, well-labeled NDT image dataset covering diverse defect types and severity levels before model development begins.
- Engage airworthiness and quality engineers early to align on defect taxonomy and acceptable false-positive/negative thresholds.
- Design the system as decision-support rather than autonomous disposition, keeping a qualified human inspector in the loop for sign-off.
- Plan a phased regulatory approval pathway with EASA or FAA from project inception to avoid late-stage compliance blockers.
How this goes wrong
- Insufficient labeled training data leads to high false-negative rates, missing real defects and creating safety risk.
- Model trained on one aircraft type or sensor modality fails to generalise to other platforms, requiring costly retraining.
- Lack of integration with MRO ERP or maintenance records means findings are not actioned in workflow, reducing operational impact.
- Regulatory non-acceptance: certification authorities (EASA/FAA) do not approve AI-assisted findings without extensive validation evidence, blocking deployment.
When NOT to do this
Do not deploy this system in an organisation that lacks certified NDT Level II/III personnel to validate AI findings, the absence of qualified human oversight invalidates airworthiness sign-off and creates unacceptable safety liability.
Example vendors
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