MétaCan
Menu
Back to cohort

Characterization of single- and multilayer cold-spray coating of Zn on AZ31B

2021· article· en· W3155865589 on OpenAlexafffund
Bahareh Marzbanrad, Ehsan Toyserkani, Hamid Jahed

Bibliographic record

VenueSurface and Coatings Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGas dynamic cold sprayCoatingMaterials scienceCharacterization (materials science)MetallurgyComposite materialNanotechnology

Abstract

fetched live from OpenAlex

Zinc, a soft material with a low melting point and high corrosion resistance, was coated onto AZ31B Mg alloy using different cold spraying process parameters. The physical and mechanical properties of the resulting Zn/AZ31B samples were then investigated to explore the effect of the process parameters on the microstructural and mechanical characteristics. The results obtained via X-ray diffraction show the formation of an intermetallic material at the interface of Zn/AZ31B even at low process temperatures. In addition, spherical droplets of Zn were observed at the surface, confirming the partial melting of Zn particles during the impact. This partial melting is believed to lead to the formation of intermetallic compounds during solidification. To engineer the residual stress induced in the cold spraying process, a thin layer of dense Zn was then used as an intermediate layer before coating with Al7075, forming a multilayered surface of Al7075/Zn/AZ31B. Because of the higher thermal expansion coefficient of Zn compared with those of Al7075 and AZ31B, beneficial compressive residual stress could be created in all three layers of this novel multilayer deposition. Without the Zn interlayer, Al7075/AZ31B under the same coating parameters exhibited undesirable tensile residual stress in the substrate.

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 score0.660

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 head0.009
GPT teacher head0.212
Teacher spread0.204 · 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

Citations23
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueSurface and Coatings TechnologySame topicHigh-Temperature Coating BehaviorsFrench-language works237,207