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Record W2098795293 · doi:10.1142/s0217984908015474

DETECTION AND EVALUATION OF DELAMINATION USING ACOUSTIC EMISSION METHOD IN THERMALLY SPRAYED COATING UNDER TENSILE STRESS

2008· article· en· W2098795293 on OpenAlexfundno aff
Yoshiko Shinhara, Kozo Ohtani, Tarou Tokuda, Fumihiro Suzumura, Mitsuo Kido

Bibliographic record

VenueModern Physics Letters B · 2008
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsnot available
FundersMcGill University
KeywordsMaterials scienceDelamination (geology)CoatingComposite materialUltimate tensile strengthAcoustic emissionStress (linguistics)Fiber pull-outCeramicComposite number

Abstract

fetched live from OpenAlex

AE was measured under a static tensile load for the thermally sprayed ceramic coating in order to investigate the relationship between the detected m-value and the destruction (delamination) of the sprayed coating. This was an effective method to detect and evaluate the delamination of a sprayed coating based on the m-value, because changes of the m-value were closely related in thermally sprayed coatings. Stress level of the delamination analyzed by AE method was almost the same as that predicted by the ESPI method. The generation of delamination was confirmed by cross-section observation of the coating using both stress levels. Therefore, the ESPI method seems to be an effective way to define and evaluate the delamination of sprayed coating determine.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.042
GPT teacher head0.293
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2008
Admission routes1
Has abstractyes

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