Characterization of anisotropic materials with the Laser-Ultrasonic technique using the “in plane” and the “out of plane” measurements of the sample surface displacement
Bibliographic record
Abstract
The progress of lasers for the generation as for the detection of ultrasonic waves opened ways for many applications where measurements need to be contactless and at a distance. For example, the Laser-Ultrasonic technique is promising for the mechanical characterization of anisotropic materials. Nevertheless, the signal processing as the data analysis becomes more complex than in the case of the classical technique that uses piezoelectric transducers. The generation and detection being punctual on the sample surface, the measured ultrasonic wave velocities correspond to group velocities. That point complicates the elastic tensor determination. In that work, a well adapted formulation is presented and is applied to characterize anisotropic materials, in particular, a composite material where its structure complicates the problem. The localization of wave front arrivals becomes intricate due to the inhomogeneity of such materials regarding their multi-layered structures and the large bandwidth of the acoustical source. In this work, we present measurements of waveforms obtained with a heterodyne laser interferometer that allows us to measure the “in-plane” and the “out-of-plane” displacements of the sample surface. Both measurements authorize a better localization of wave front arrivals. Interests of this technique are underlined.
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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 | 0.001 | 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".