Laser-Ultrasonic Characterization of Fiber Reinforced Composites: Effect of the Generation Laser Transverse Modes
Bibliographic record
Abstract
Laser‐ultrasonics has been used with success for the microstructure evaluation of metals by measuring ultrasonic attenuation and velocity. Extension of this technique to fiber reinforced polymer composites could provide useful information such as fiber contents and porosity. Ultrasonic generation in composite materials is usually performed by using a pulsed TEA‐CO2 laser operated on several transverse modes. The intensity profile of this laser could change from shot‐to‐shot, thus affecting the ultrasonic generation pattern and the accuracy of attenuation measurement. Two solutions are possible to overcome this problem. The first reported approach consists in numerically correcting for the diffraction effect by using a Monte Carlo integration after simultaneous acquisition of the ultrasonic signal and the laser mode pattern. The second reported approach consists in modifying the laser resonator to get a single transverse mode while maintaining high energy per pulse, and to make the diffraction correction much simpler. Both approaches have been tested on a bare carbon epoxy plate with laser generation in the thermoelastic regime. The results obtained from using either approaches show a significant improvement and are compared to those obtained by conventional ultrasonics.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".