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Record W2064211107 · doi:10.1115/imece2014-36827

Development and Oxidation Test of Metal Mesh Reinforced Ceramic Composite Material

2014· article· en· W2064211107 on OpenAlexaff
Mitch Kibsey, Xiao Huang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsCarleton University
Fundersnot available
KeywordsMaterials scienceCeramicComposite materialCubic zirconiaCeramic matrix compositeComposite numberOxideThermal barrier coatingIsothermal processCoatingMetallurgy

Abstract

fetched live from OpenAlex

As part of an ongoing research development at Carleton University in ceramic matrix composites (CMCs) for high-temperature gas turbine applications, it was recognized that the performance of an oxide matrix could be improved by incorporating a metal reinforcement material. For this reason, a low cost CMC was created by reinforcing a yttria-stabilized zirconia (7YSZ) ceramic matrix with a Hastelloy X (HX) wire mesh. The CMC was manufactured by coating the HX mesh with a NiCrAlY bond coat, and then 7YSZ ceramic matrix, both using plasma spraying. The bond coat was employed to improve bonding and also to act as an oxygen diffusion barrier. In order to evaluate the performance of the HX/7YSZ composite at high temperatures, isothermal and cyclic oxidation tests were carried out for 1000 hours at 1050°C. The results showed that oxidation resistance was improved by vacuum heat treatment prior to testing due to the formation of stable thermally grown oxides (TGO) on the NiCrAlY bond coat. In the cyclic oxidation test, differences in thermal expansion coefficients caused cracking at interfaces between mesh/bond coat and bond coat/7YSZ. Minimizing the effect of thermal expansion by better material combination, as well as modifying manufacturing methods will allow for improved performance of metal mesh reinforced CMCs.

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 score0.014
Threshold uncertainty score0.334

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 head0.007
GPT teacher head0.199
Teacher spread0.193 · 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
Published2014
Admission routes1
Has abstractyes

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