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Record W2524450695 · doi:10.1117/1.oe.56.4.041304

Comparative study of microlaser excitation thermography and microultrasonic excitation thermography on submillimeter porosity in carbon fiber reinforced polymer composites

2016· article· en· W2524450695 on OpenAlexafffund
Hai Zhang, Henrique Fernandes, Ulf Haßler, Clemente Ibarra‐Castanedo, Marc Genest, F. Robitaille, Simon Joncas, Xavier Maldague

Bibliographic record

VenueOptical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsÉcole de Technologie SupérieureNational Research Council CanadaUniversity of OttawaUniversité Laval
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoNatural Sciences and Engineering Research Council of CanadaConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsThermographyMaterials scienceComposite materialJoint (building)InfraredImage stitchingExcitationCore (optical fiber)OpticsStructural engineering

Abstract

fetched live from OpenAlex

Stitching is used to reduce incomplete infusion of T-joint core (dry-core) and reinforce T-joint structure. However, it may cause new types of flaws, especially submillimeter flaws. Thermographic approaches including microvibrothermography, microlaser line thermography, and microlaser spot thermography on the basis of pulsed and lock-in techniques were proposed. These techniques are used to detect the submillimeter porosities in a stitched T-joint carbon fiber reinforced polymer composite specimen. X-ray microcomputed tomography was used to validate the thermographic results. Finally an experimental comparison of microlaser excitation thermography and microultrasonic excitation thermography was conducted.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.007
GPT teacher head0.203
Teacher spread0.195 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2016
Admission routes2
Has abstractyes

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