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Cold Spray Characteristics of Commercially Pure Ti and Ti-6Al-4V

2010· article· en· W2008621116 on OpenAlexaff
Wilson Wong, Ahmad Rezaeian, Éric Irissou, J.-G. Legoux, Stephen Yue

Bibliographic record

VenueAdvanced materials research · 2010
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsNational Research Council CanadaMcGill University
Fundersnot available
KeywordsMaterials scienceCoatingIndentation hardnessPorosityGas dynamic cold sprayNozzleComposite materialDeposition (geology)MetallurgyHigh-speed steelMicrostructure

Abstract

fetched live from OpenAlex

Ti and Ti-6Al-4V coatings were deposited by cold gas dynamic spray process using nitrogen as propellant gas. For Ti, the inlet gas temperature and pressure were varied for two different powder morphologies in such a way that the average particle velocity ranged from ~600 to 850 m/s. In addition, the nozzle traverse speed was varied. For all conditions, the deposition efficiency, the porosity, and the microhardness were measured. It is shown that the porosity level decreased as the gas temperature and pressure increased, whereas the velocity, deposition efficiency, and microhardness increased. Furthermore, it is observed that a lower nozzle traverse speed engendered a softer coating. The coating adhesion on a grit 24 Al2O3 blasted mild steel surface was established to be greater than 78.8 MPa. In addition, a Ti-6Al-4V coating was produced and was determined to be slightly more porous compared to a CP Ti coating.

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 score0.002
Threshold uncertainty score0.007

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)0.0020.000

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 head0.022
GPT teacher head0.324
Teacher spread0.302 · 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

Citations86
Published2010
Admission routes1
Has abstractyes

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