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Record W3160121438 · doi:10.31399/asm.cp.itsc2021p0036

MCrAlX (X = Y, Hf and Si) Bond Coats by Cold Spray for High Temperature Applications

2021· article· en· W3160121438 on OpenAlexaff
C. V. Cojocaru, Maniya Aghasibeig, Éric Irissou

Bibliographic record

VenueThermal spray · 2021
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsThermal sprayingMaterials scienceGas dynamic cold sprayNozzleDeposition (geology)Spray nozzleSpray formingIsothermal processSolution precursor plasma sprayMetallurgyOxideRaw materialThermal barrier coatingChemical engineeringComposite materialMechanical engineeringCoatingThermodynamicsChemistryCeramicMicrostructure

Abstract

fetched live from OpenAlex

Abstract MCrAlX powder compositions (M=Ni, Co and X=Y, Hf, Si or combination) are often thermally sprayed (TS) via vacuum plasma spray (VPS), low pressure plasma spray (LPPS) or high velocity oxy-fuel (HVOF) to produce high temperature oxidation and hot corrosion resistant bond coats (BC) for thermal barrier coatings (TBCs). Cold spray (CS) technology is currently considered as a promising alternative to the traditional TS solutions having the advantage of delivering oxide-free and very dense metallic coatings at relatively lower costs compared to VPS and LPPS. Here, we first present high-pressure CS deposition of NiCoCrAlY and NiCoCrAlYHfSi and discuss the influence of feedstock properties on the deposited BCs. CFD numerical simulation is employed to tailor the spray conditions based on the feedstock characteristics. Secondly, we present the laser assisted cold spray (LACS) deposition of NiCoCrAlYHfSi BCs using a low-pressure CS system. We show that LACS can be successfully used to deposit this particular powder while eliminating nozzle erosion and low deposition efficiency disadvantages observed during conventional CS. Lastly, high temperature isothermal oxidation of a TBC architecture having LACS BC is presented.

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 categoriesMeta-epidemiology (narrow)
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 score0.067
Threshold uncertainty score1.000

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 head0.212
Teacher spread0.206 · 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.

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

Citations2
Published2021
Admission routes1
Has abstractyes

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