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Influence of V concentration in TiAlSiVN coating on self-lubrication, friction and tool wear during two-pass dry turning of austenitic steel 316 L

2024· article· en· W4391480505 on OpenAlex
Ch Sateesh Kumar, Gorka Urbikaín, Filipe Fernandes, Abbas AL Rjoub, Luís Norberto López de Lacalle

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

VenueTribology International · 2024
Typearticle
Languageen
FieldEngineering
TopicMetal and Thin Film Mechanics
Canadian institutionsPolytechnique Montréal
FundersAgencia Estatal de InvestigaciónFundação para a Ciência e a TecnologiaEuropean Regional Development FundMinistério da Ciência, Tecnologia, Inovações e ComunicaçõesEuskal Herriko UnibertsitateaEuropean Commission
KeywordsLubricationMaterials scienceCoatingMetallurgyAustenitic stainless steelMachiningTool wearAusteniteFlankComposite materialCorrosionMicrostructure

Abstract

fetched live from OpenAlex

The present work investigates the performance of TiAlSiVN coating with 5 and 11 at% of V concentration deposited on the Al2O3/SiC cutting tools during dry turning of austenitic 316 L stainless steel. The maximum flank wear reduction compared to the uncoated tool for coated tools with 11% and 5% V concentration was 85% and 67%, respectively. The Raman analysis indicated the formation of V2O5 in the cutting zone, which helps to reduce friction and machining forces for the coated tools. Overall, the presence of higher V content (11 at.%) enhances the self-lubrication behaviour of the TiAlSiVN coating, accounting to lower fluctuations in cutting forces, superior surface finish, and lower flank wear when compared to the TiAlSiV5N coated and uncoated cutting tools.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.323

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 head0.006
GPT teacher head0.230
Teacher spread0.223 · 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