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Record W2146778386 · doi:10.1002/sia.2733

Features of self‐organization in nanostructuring PVD coatings on a base of polyvalent metal nitrides under severe tribological conditions

2008· article· en· W2146778386 on OpenAlexaff
A. A. Kovalev, Dmitry Wainstein, German Fox‐Rabinovich, Stephen C. Veldhuis, Kenji Yamamoto

Bibliographic record

VenueSurface and Interface Analysis · 2008
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials sciencePhysical vapor depositionTribologyCoatingNitrideMetallurgyMachiningX-ray photoelectron spectroscopyComposite materialNiobiumChemical vapor depositionTungstenNanotechnologyLayer (electronics)Chemical engineering

Abstract

fetched live from OpenAlex

Abstract Applications of quaternary nitride nanomultilayered coatings result in a significant improvement in tool life as well as wear behavior of ball nose end mills under conditions of high‐speed machining of hardened steels. Features of self‐organization in the nanostructuring monolayer (Al 67 Ti 33 )N and nanolaminate multilayer (AlCrTi)N/(CrN, WN or NbN) physical vapor deposition (PVD) coatings have been investigated under severe frictional conditions associated with high temperatures and stresses, which are typical for high‐speed cutting (HSC). Structure and phase transformations on wear surface have been studied using XPS. Wear behavior of the coating has been investigated under severe conditions of HSC of 1040 steel. Results show that the enhancement of nonequilibrium processes during friction leads to a dominating formation of protective triboceramics on the basis of sapphire‐like and tungsten and niobium polyvalent oxides with the structure that improves the wear performance critically. Copyright © 2008 John Wiley & Sons, Ltd.

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 score0.248
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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

Citations15
Published2008
Admission routes1
Has abstractyes

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