Robotized phased-array inspection of sandwich-structured aircraft components
Bibliographic record
Abstract
be challenging. The manual inspection techniques usually used in this context are time-consuming and can lead to inconsistent results. L3Harris and the Centre Technologique en Aérospatiale (CTA) have developed a novel robotized solution to conduct efficient ultrasonic inspection of sandwich-structured components. The system is based on collaborative robots to enhance safety for both components and operators and uses custom-made probe-holders designed to achieve smooth scanning movements and proper coupling conditions. This paper focuses on the ultrasonic aspects of the solution: customized phased-array probes, specific firing sequences and novel data analysis techniques. The objectives were to reduce the inspection time, lower the number of different probes and passes required to cover the full components, obtain complete ultrasonic images of the components ensuring data traceability over time, and provide guidance for the diagnostic. The solution uses a pair of custom 1.5D linear phased-array probes mounted on conformable wedges. The firing technique varies the active aperture during the scan. This allows for simultaneous acquisition of pulse-echo (PE) data in the lower and upper skins, and through-transmission (TT) data through the sandwich, during a single pass. A novel analysis algorithm was developed to automatically compare the collected A-scan signals in each of the three available channels (PE top, PE bottom and TT) to reference signals. Depending on the similitude of the signal triplets with the references, each inspected pixel can be classified in one situation (pristine or defective) and color-coded accordingly. A diagnostic map is then generated and can be used by the inspector in correlation with more classical data (A-scans, B-scans, and C-scans) to make an informed decision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".