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Record W4399424009 · doi:10.1117/12.3013695

Advanced NDT inspection approach for aircraft composite panels

2024· article· en· W4399424009 on OpenAlexaff
Angelos Plastropoulos, Muhammet E. Torbali, Nicolas P. Avdelidis, Clemente Ibarra‐Castanedo, M. Klein, Xavier Maldague

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsNondestructive testingAircraft maintenanceComposite numberComputer scienceEngineeringReliability engineeringAeronauticsPhysics

Abstract

fetched live from OpenAlex

The use of composite materials in aircraft manufacturing is increasing, driven by the need for reduced weight and improved fuel efficiency. This trend extends to military aircraft, as defence manufacturers and suppliers seek to minimise operating costs. However, these composites are susceptible to various defects, which require the use of advanced Non-Destructive Testing (NDT) techniques for their detection and evaluation. Phased Array Ultrasonic Testing (PAUT) is such an NDT technique that provides precise defect characterisation, including delaminations, cracks, voids, and porosity. Active Thermography (AT) is an alternative NDT technique that is emerging as a non-contact and full-field technique for identifying such defects. PAUT and AT are complementary techniques, and a synergistic approach that combines their capabilities promises enhanced aircraft safety and composite structure reliability. This paper presents research findings using PAUT equipment and active infrared thermography for identifying and evaluating defects in composites. The presented combined approach is expected to improve aircraft safety and composite structure reliability in aerospace applications.

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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.559
Threshold uncertainty score0.460

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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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
GenreMethods

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

Citations1
Published2024
Admission routes1
Has abstractyes

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