Nondestructive Evaluation Of Composite Part Details Using Digital Acoustic Video Evaluation
Bibliographic record
Abstract
The universal applicability of ultrasonic inspection to aircraft components has helped establish it as an essential nondestructive method. Composite detail parts such as attach pieces, stiffeners, and intercostals generally undergo manual contact ultrasonic inspection due to their complex shapes and relatively small size. These manual inspections involve scanning transducers as small as 0.635 cm (0.25 inch) in diameter across the part surface in order to provide 100% inspection coverage. These inspections are labor-intensive, tedious, and costly in terms of the time involved. Usually, the anomalies and defects associated with these parts are relatively simple to characterize, with the radius areas being the most difficult. Digital acoustic video evaluation essentially provides an ultrasonic "camera" capable of much broader coverage than the conventional transducer arrangement. This inspection is performed by placing the part in a tank of water while articulating it between a relatively large transducer and the digital acoustic camera. The results can then be viewed in real time. This method is, of course, faster than the conventional manual pulse-echo ultrasonic method by several times. In this paper, we will give the results of a study involving several part details having varied types of defects. We will also demonstrate the congruency between the conventional method and the digital acoustic video method as far as sensitivity is concerned. This is a major factor when deciding whether the faster method should replace the slower one.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".