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Record W1966249086 · doi:10.1139/p07-082

Experimental and theoretical basis of Lamb waves and their applications in material sciences

2007· article· en· W1966249086 on OpenAlexaffvenue
Jeffrey M. Sadler, Roman Gr. Maev

Bibliographic record

VenueCanadian Journal of Physics · 2007
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsLamb wavesPhysicsAcousticsRayleigh waveFocus (optics)Nondestructive testingAcoustic waveCharacterization (materials science)Mechanical waveWave propagationLongitudinal waveOptics

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.121

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.206
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2007
Admission routes2
Has abstractyes

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