Experimental and theoretical basis of Lamb waves and their applications in material sciences
Bibliographic record
Abstract
The subject of Lamb waves contains a vast field of literature comprising many individual topics, with the current focus being the effective creation and use of Lamb waves in the fields of material characterization and nondestructive evaluation (NDE). This review chooses to focus on the more recent literature dealing with Lamb waves, giving introductions to a variety of topics. Because of the large amount of literature dealing with Lamb waves, many of the sections of this paper could themselves be expanded into their own literature review. This review begins with a brief introduction of Lamb waves comparing them to the acoustic bulk wave, and Rayleigh wave, and outlines the physics of a guided wave. It discusses the advantages of using guided waves, and theoretical techniques to model Lamb waves. In addition, the review discusses some of the various methods for the detection and creation of Lamb waves; techniques to detect, identify, and extract the mode from the acoustic signal; the use of Lamb waves in material characterization; flaw detection and flaw measurement; and finally examines the scattering of Lamb waves at plate ends and joints. While much of this work is experimentally based in nature, this review has attempted to also include theoretical work when possible. PACS Nos.: 43.90.+v, 81.70.Cv
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".