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Record W2087876215 · doi:10.1002/pc.10212

Nondestructive evaluation methods for damage assessment in fiber‐metal laminates

2000· article· en· W2087876215 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePolymer Composites · 2000
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsCarleton UniversityNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceNondestructive testingAerospaceComposite materialGLAREEddy currentCorrosionUltrasonic sensorAcousticsEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Abstract The Structures, Materials and Propulsion Laboratory of the NRC Institute for Aerospace Research (IAR) is engaged in a collaborative project with Bombardier Aerospace. The main objective of the project is to evaluate the potential of applying fiber‐metal laminates (FML) to aircraft types manufactured by Bombardier. As a part of this project, nondestructive evaluation (NDE) procedures have been developed and used at IAR to determine the extent of damage caused by impact, corrosion and fatigue loads in a commercial FML material (GLARE). X‐rays using radioopaque fluids as well as conventional and air‐coupled ultrasonic and eddy current methods have been investigated. This report describes the NDE procedures employed at IAR to assess damage in FML and provides examples of the results obtained utilizing each of the inspection methods and the damage types investigated. Also, the ability of the investigated NDE methods to determine damage size and the accuracy of damage measurements is discussed.

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.

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)0.001
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: none
Teacher disagreement score0.547
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.360
Teacher spread0.339 · 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