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Record W4411863933 · doi:10.58286/31525

Development of NDT Method for Porosity Evaluation in Composite Materials

2025· article· en· W4411863933 on OpenAlexafffund
A. Chahbaz, René Sicard

Bibliographic record

Venuee-Journal of Nondestructive Testing · 2025
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsThinkpath Engineering Services (Canada)
FundersConcordia UniversityPolytechnique MontréalNatural Sciences and Engineering Research Council of CanadaMitacsÉcole de technologie supérieure
KeywordsNondestructive testingPorosityComposite numberMaterials scienceComposite materialPhysics

Abstract

fetched live from OpenAlex

Porosity is a critical defect in composite materials used in aerospace structures, influencing mechanical strength, fatigue performance, and overall component reliability. Standard aerospace practice demands porosity levels below 2% [1, 2]. Despite the availability of destructive and non-destructive testing methods, there exists no industry-wide standardized procedure for porosity quantification. This article presents a comprehensive and multi-phase research effort led by TecScan, in collaboration with academic partners and researchers, to develop a reliable and non-destructive ultrasonic testing method for quantifying porosity in carbon-fiber reinforced polymer (CFRP) composites using ultrasonic attenuation. The focus of this work is to meet the stringent NDT and quality control demands of aerospace-grade materials.

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)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.049
GPT teacher head0.356
Teacher spread0.307 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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