A laser-ultrasonic inspection system for large structures fabricated by automated fiber placement
Bibliographic record
Abstract
Automatic fiber placement (AFP) is an innovative method for fabricating monolithic large-scale composite structures, such as the big and long barrels that can be assembled to make the fuselage of an airplane. Laser-ultrasonics and laser tapping, which use lasers for generation and detection of ultrasound, are established inspection techniques that are very efficient for detecting flaws (delaminations, disbonds) in complex shape composite structures. A laser-ultrasonic system with a configuration well adapted for the inspection of these big barrel structures was designed, built and installed at the National Research Council of Canada facility, located at Mirabel, Qc, adjacent to composite manufacturing operations. The laser-ultrasonic system is mounted on a long cantilever structure that is inserted into the fuselage. The probing head includes a rotating mirror assembly allowing 360° and one meter wide or more scanning of the whole internal wall of the barrel. The system can also inspect the external wall, when the barrel is mounted on carriage allowing rotation around its axis, as well as various smaller complex shape parts. This inspection system can be operated in both laser-ultrasonic and laser-tapping modes, the latter being particularly useful for honeycombs. A detailed description of the system is presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".