Detection and Characterization of Flaws in Stainless Steel by the Laser-Ultrasonic Synthetic Aperture Focusing Technique
Bibliographic record
Abstract
The performance of a laser ultrasonic system applied to the detection and characterization of stress corrosion cracks and welding discontinuities in stainless steel is reported. A 50 mJ long pulse laser coupled with a confocal Fabry-Perot interferometer and the synthetic aperture focusing technique are used to enhance weak discontinuity related signals. First, stainless steel plates containing stress corrosion cracks with widths of less than 0.03 mm (1.2 × 10-3 in.) and depths ranging from 0.5 to 5 mm (0.02 to 0.2 in.) are tested. Both longitudinal and shear waves are used in synthetic aperture focusing technique processing to detect and characterize these cracks from the surface opposite cracking. Images at the breaking surface reconstructed by shear waves provide very detailed structures of branched tight cracks, which are comparable to results obtained by liquid penetrant testing. This excellent imaging is linked to the presence of crosslike features appearing in B-scans that are interpreted and explained. Secondly, a welded stainless steel plate is tested and various weld discontinuities with sizes of about 1 mm (0.04 in.) are detected. Although the material strongly attenuates ultrasound in this case, laser ultrasonics combined with the synthetic aperture focusing technique successfully detects, locates and sizes these discontinuities. These results are compared with those obtained by X-ray computed tomography.
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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".