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Record W2028773117 · doi:10.1115/gt2007-27852

Evaluation of Plasma Sprayed Thermal Barrier Coatings Using NDE and SEM

2007· article· en· W2028773117 on OpenAlexaff
A. Fahr, Catalin Mandache, Marc Genest, Weijie Chen, Xijia Wu, John A. Thornton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsThermal barrier coatingMaterials scienceSpallationComposite materialEddy currentCeramicNondestructive testingEddy-current testingTurbine bladeSuperalloyThermographyInfraredPlasmaThermalTurbineMicrostructureOpticsNeutronMechanical engineering

Abstract

fetched live from OpenAlex

Thermal barrier coatings (TBC) are used to protect the hot section components of gas turbine engines from high temperatures. A TBC system consists of a ceramic topcoat and a metallic bond coat sprayed or deposited onto the metal substrate. TBC failure is often associated with oxidation of the metallic bond coat at elevated temperatures via formation of thermally grown oxides (TGO) that cause internal stresses leading to the final spallation of the TBC. The present study explores the application of eddy current and infrared thermal imaging techniques for the detection of TGO in thermally-exposed TBC with a view of finding the damage criteria and a suitable solution for nondestructive evaluation (NDE) of TBC. The eddy current technique is based on the induction of an electromagnetic field and is sensitive to minute changes in electrical or magnetic properties of the test piece while infrared thermal imaging is based on thermal diffusion process and measures small differences in surface temperature. The NDE results are validated through destructive testing and microscopic examination of the TBC samples in as-sprayed condition and after exposure to elevated temperatures.

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)0.001
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: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.406

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 head0.027
GPT teacher head0.280
Teacher spread0.252 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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