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Record W4294647031 · doi:10.31399/asm.cp.itsc2003p1499

The Use of Acoustic Emission Techniques for Characterizing Failure Mechanisms of Thermal Barrier Coatings Under Thermal Cycling Conditions

2003· article· en· W4294647031 on OpenAlexaff
F. Gitzhofer, Maher I. Boulos

Bibliographic record

VenueThermal spray · 2003
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsThermal barrier coatingTemperature cyclingMaterials scienceCoatingReliability (semiconductor)Jet engineThermalWork (physics)Acoustic emissionStress (linguistics)Forensic engineeringComposite materialMechanical engineeringEngineeringPower (physics)

Abstract

fetched live from OpenAlex

Abstract Thermal barrier coatings are mainly used to protect underlying alloys from heat and chemical aggressions, especially in jet turbines and diesel engines. The challenge in TBCs’ applications is to use them to protect the upper moving turbine’s blades where their surface temperature can reach 1200°C. The limiting effect is the reliability of these coatings. The work described in this paper is a continuation of our earlier research work, which is focused on the use of Acoustic Emission Technique to assess the long-term behavior of thermal barrier coatings under thermal cycling conditions. Emphasis is placed in this presentation on the comparison of different signal processing techniques and the evaluation of their potential usefulness for the prediction of the coating behavior and failure modes. The work is carried out in parallel with a finite element modeling study of the thermal and stress distribution in the coating, which provides a valuable insight in the coating stress distribution prior to failure.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.860

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 head0.020
GPT teacher head0.249
Teacher spread0.229 · 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
Published2003
Admission routes1
Has abstractyes

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