MétaCan
Menu
Back to cohort
Record W2153954478 · doi:10.1116/1.4737619

Study of the fatigue wear behaviors of a tungsten carbide diamond-like carbon coating on 316L stainless steel

2012· article· en· W2153954478 on OpenAlexaff
Ying Chen, Xueyuan Nie

Bibliographic record

VenueJournal of Vacuum Science & Technology A Vacuum Surfaces and Films · 2012
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMaterials scienceCoatingTungsten carbideTribologyComposite materialDiamond-like carbonScratchMetallurgyScanning electron microscopeCeramic

Abstract

fetched live from OpenAlex

A tungsten carbide (WC) diamond-like carbon (DLC) hard coating has higher lubricity and resilience than other types of nitride-, carbide- and oxide-based ceramic coatings. To evaluate the anti-wear properties of the DLC coating under extremely high impact-sliding loading conditions, a newly developed method, called the cycled inclined impact-sliding test, is introduced and utilized to study fatigue wear behaviors of the DLC coating under a combination of an impact force, Fi, and pressing force, Fp (Fi/Fp = 200 N/400 N), for up to 10 000 cycles. A 10 mm steel (AISI 52100) bearing ball is used as the impacting and sliding object. Due to the low coefficient of friction of the WC DLC coating against the steel counterface, which creates a reduced tangential force on the coating surface and less tensile stress within the coating, the WC DLC coating exhibits high endurance when impacted in dry air conditions. A scanning electron microscopy study showed that different types of fatigue wear cracks appeared and were distributed in the different areas (head and tail parts) of the impact and sliding tracks. Most of those cracks penetrated the coating and caused sawtooth-like deformation in the substrate. Material transfer from the counterface ball was not detected by energy-dispersive x-ray spectroscopy.

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)0.004
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 score0.066
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.297
Teacher spread0.273 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Vacuum Science & Technology A Vacuum Surfaces and FilmsSame topicDiamond and Carbon-based Materials ResearchFrench-language works237,207