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Record W2293988027

Linear sliding wear behavior of aluminium matrix composites reinforced by particulates

2013· preprint· en· W2293988027 on OpenAlexaff
Riad Khettabi, Jacek Litwin, Jacques Masounave

Bibliographic record

VenueDSpace (Centre National De La Recherche Scientifique) · 2013
Typepreprint
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMaterials scienceComposite materialRubbingLubricationParticulatesVolume fractionTribologyAluminiumComposite numberMixing (physics)Matrix (chemical analysis)
DOInot available

Abstract

fetched live from OpenAlex

Linear sliding wear behavior studied for metal composites. Different types of composites were tested and three different fabrication techniques: two foundry techniques with different volume fraction and particulates size and thermal projection. The results show that the lubrication has a strong effect on the wear rate. The composites did not wear when tested under oil lubrication conditions, while the cable wore rapidly. The opposite phenomena occurred when dry tests were performed: the cable was protected while the specimen wore quickly. The critical volume fraction, i.e. the minimum volume fraction of particulates for protecting the composites, is on the order of 10%, whatever the size of the reinforcement. This minimum volume fraction does not seem to significantly depend on the size of the particulates. Finally, it was observed that the wear rate of the composite is the lowest for composites reinforced by a low percentage of particulates. A mechanical model describes the behavior of both partners (cable and MMC).

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.307
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
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 head0.080
GPT teacher head0.325
Teacher spread0.245 · 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.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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