MétaCan
Menu
Back to cohort
Record W2014149360 · doi:10.4236/msa.2012.311110

Tribological Behavior of Stellite 720 Coating under Block-on-Ring Wear Test

2012· article· en· W2014149360 on OpenAlex
Rong Liu, Qi Yang, Feng Gao

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMaterials Sciences and Applications · 2012
Typearticle
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsNational Research Council CanadaCarleton University
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsStelliteMaterials scienceCoatingTribologyMetallurgyLayer (electronics)Composite materialAlloyScanning electron microscopeIndentation hardnessMicrostructure

Abstract

fetched live from OpenAlex

Stellite 720 is a high-carbon Co-Cr-Mo alloy, designed for severe wear/corrosion environments. This article presents a study of the Stellite 720 coating on stainless steel 304 under block-on-ring wear test. The coating is deposited through a slurry/powder metallurgy sintering process. Micro-hardness indentation test is conducted on the cross section of the coating specimen to investigate the hardness of individual phases of the coating material and the dilution effect of the substrate material on the coating layer. The tribological behavior of the coating under low and high load wear is investigated. The worn surfaces of the coating specimens are analyzed using a Philips XL30S FEG scanning electron micro- scope (SEM) with an EDAX energy dispersive X-ray (EDX) spectroscopy system. The experimental results are discussed to explore the wear mechanisms of the Stellite 720 coating under block-on-ring wear.

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 score0.008
Threshold uncertainty score0.260

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 head0.024
GPT teacher head0.266
Teacher spread0.241 · 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