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Record W3197350872 · doi:10.1051/aacus/2021031

Laser-induced ultrasonic measurements for the detection and reconstruction of surface defects

2021· article· en· W3197350872 on OpenAlexaff
Feiming Qian, Guangzhen Xing, Ping Yang, Pengcheng Hu, Limin Zou, Triantafillos Koukoulas

Bibliographic record

VenueActa Acustica · 2021
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsNational Research Council Canada
FundersKey Technologies Research and Development ProgramNatural Science Foundation of Heilongjiang ProvinceChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsMaterials scienceUltrasonic sensorLaserOpticsNondestructive testingUltrasonic testingAcousticsPhysics

Abstract

fetched live from OpenAlex

Laser-induced ultrasonic measurement is a non-contact non-destructive technology that can be employed for the testing and assessment of surface defects. In order to improve the correct identification of defects, the full matrix capture (FMC) and total focusing method (TFM) are applied on the imaging process. FMC data includes A-scans resulting from the combination of all measurement axes defined by the sequential generation and detection of utilized laser beams in the system. In this paper, an aluminium block with four holes whose diameters range from 1 mm to 2.5 mm is assessed through B-scans, the synthetic aperture focusing technique (SAFT) and FMC/TFM. The results demonstrate that the FMC/TFM technology can significantly improve the imaging quality and signal-to-noise ratio (SNR). In addition, this method has higher lateral resolution and larger imaging range compared with traditional B-scans.

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 score0.147
Threshold uncertainty score0.258

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 head0.022
GPT teacher head0.219
Teacher spread0.197 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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