MétaCan
Menu
Back to cohort
Record W4411339485 · doi:10.3390/lubricants13060264

Wear of Stellite 6 Coatings Produced with High-Velocity Oxygen Fuel at Elevated Temperatures

2025· article· en· W4411339485 on OpenAlexafffund
Alejandra Islas Encalada, Pantcho Stoyanov, Mary Makowiec, Christian Moreau, Richard R. Chromik

Bibliographic record

VenueLubricants · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsConcordia UniversityMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaConsortium de Recherche et d’innovation en Aérospatiale au QuébecMcGill University
KeywordsStelliteMaterials scienceOxygenMetallurgyChemical engineeringChemistryMicrostructureEngineering

Abstract

fetched live from OpenAlex

This paper investigates the tribological behavior of Stellite 6 coatings produced with high-velocity oxygen fuel (HVOF), with an emphasis on the transition between severe and mild wear regimes and the glaze layer formation. The development of these coatings involved two spray parameters modifying the oxygen fuel ratio and three post-heat treatment conditions at temperatures ranging between 600 °C and 1150 °C. The coatings were tested under conditions varying the normal load, temperature, sliding distance, and testing temperatures (up to 300 °C). The results show that the coating obtained from the HVOF process exhibited a microstructure different from the conventional bulk Co-alloys, significantly impacting the wear performance. The coating post-processing was essentialto enhance wear resistance at elevated temperatures.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.196
Teacher spread0.192 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueLubricantsSame topicAdvanced materials and compositesFrench-language works237,207