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Record W4205111070 · doi:10.3390/mi13010070

An Experimental Study of Micro-Dimpled Texture in Friction Control under Dry and Lubricated Conditions

2021· article· en· W4205111070 on OpenAlex

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

VenueMicromachines · 2021
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsUniversity of Calgary
FundersVINNOVA
KeywordsDimpleMaterials scienceReciprocating motionLubricationTexture (cosmology)Surface finishComposite materialReduction (mathematics)Coefficient of frictionMetallurgyComputer scienceArtificial intelligenceMathematicsBearing (navigation)Image (mathematics)

Abstract

fetched live from OpenAlex

Friction control is a vital technology for reaching sustainable development goals, and surface texturing is one of the most effective and efficient techniques for friction reduction. This study investigated the performance of a micro-dimpled texture under varying texture densities and experimental conditions. Reciprocating sliding tests were performed to evaluate the effects of the micro-dimpled texture on friction reduction under different normal loads and lubrication conditions. The results suggested that a micro-dimpled texture could reduce the coefficient of friction (CoF) under dry and lubricated conditions, and high dimple density results in a lower CoF. The dominant mechanism of the micro-dimpled texture's effect on friction reduction was discussed, and surface observation and simulation suggested that a micro-dimpled texture could reduce the contact area at the friction interface, thereby reducing CoF.

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 score0.778
Threshold uncertainty score0.456

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 head0.247
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