MétaCan
Menu
Back to cohort
Record W3194162267 · doi:10.31399/asm.cp.itsc2000p0487

Influence of Flame Parameters on Stainless Steel Coatings Properties

2000· article· en· W3194162267 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThermal spray · 2000
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsAir Liquide (Canada)
Fundersnot available
KeywordsMaterials scienceCombustionThermal sprayingGas dynamic cold sprayMicrostructureIndentation hardnessCoatingPorosityMetallurgyComposite materialOxide

Abstract

fetched live from OpenAlex

Abstract Owing to high particle velocity upon impact, and consequently low porosity and high bond strength of so-obtained coatings, HVOF spraying process is widely used to improve components life in service. However, many parameters can affect metallic coatings properties, especially un-melted particles and oxidation level. Flame parameters, such as calorific power, combustion ratio and temperature, are of prime importance. The aim of this work was focused on the influence of these parameters on stainless steel coatings characteristics. For different substrate temperatures, maintained through CO2 cooling nozzles, those parameters varied independently. Flame characteristics were computed using a simple model for propylene as fuel gas. Microstructure investigation as well as oxide content measurements and microhardness were obtained. It appeared that combustion temperature, in the range studied (2600-2750K) was not a critical factor. However, combustion ratio and calorific power greatly influenced coating properties: an increase of oxide content, and consequently a higher microhardness, was observed when combustion ratio decreased as well as when calorific power increased.

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.

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 score0.347
Threshold uncertainty score0.610

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 head0.011
GPT teacher head0.205
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