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Record W2414739937 · doi:10.21611/qirt.2014.164

RITA - Robotized Inspection by Thermography and Advanced processing for the inspection of aeronautical components

2014· article· en· W2414739937 on OpenAlexaffabout
Clemente Ibarra‐Castanedo, Pierre Servais, Adel Ziadi, M. Klein, Xavier Maldague

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsThermographyAutomated X-ray inspectionComputer scienceVisual inspectionComputer visionAutomated optical inspectionImage processingArtificial intelligenceEngineering drawingAeronauticsEngineeringInfraredOpticsImage (mathematics)

Abstract

fetched live from OpenAlex

Active thermography is often performed on a static configuration where all elements of the thermographic system, i.e. the infrared camera, the energy source and the inspected object, are standing still with respect to each other. This is very useful for the application of signal processing techniques in order to improve defect detection and characterisation. Under this configuration, the large surfaces typical of some aeronautical components, are inspected in a series of static tests that at the end are assembled together on a single reconstructed mosaic image comprising the results for the entire inspected area. However, with the fast development of innovative and ever more complex-shaped parts, the alternative dynamic active thermography configuration is gaining attention. In this case, the component of interest is inspected in motion and the acquired data can be reorganized as a pseudo-static sequence, similar to classic static data, in order to perform advanced signal processing, if required. In this work, line scan thermography inspection was investigated for the assessment of an aerospace reference panel in the framework of the Canadian-Belgian collaborative project RITA (Robotized Inspection by Thermography and Advanced processing).

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: Empirical · Consensus signal: none
Teacher disagreement score0.784
Threshold uncertainty score0.285

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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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

Citations29
Published2014
Admission routes2
Has abstractyes

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