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Record W2192614459 · doi:10.1139/tcsme-2006-0021

USINABILITÉ D’ALLIAGES LÉGERS ET DES COMPOSITES LORS DU PERÇAGE À SEC

2006· article· fr· W2192614459 on OpenAlexaffvenue
B. Balout, Victor Songméné, Jacques Masounave

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2006
Typearticle
Languagefr
FieldEngineering
TopicLubricants and Their Additives
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMachiningGraphiteMaterials scienceMetallurgyPhysicsComposite material

Abstract

fetched live from OpenAlex

L’usinage à sec est une technologie prometteuse pour réduire les coûts ďusinage et éliminer les effets néfastes des lubrifiants sur l’environnement et sur la santé. Cette technologie est cependant difficile à appliquer pour des matériaux qui collent sur l’outil ou pour des composites qui usent rapidement les outils de coupe. Dans ce travail, l’influence des paramètres de coupe, des alliages et des particules dures (SiC, Al 2 O 3 ) et douces (graphite) sur les forces de coupe et sur le mécanisme de formation des copeaux lors de l’usinage à sec est présentée. Les résultats obtenus montrent que les lois d’usinage reliant les forces aux conditions de coupe restent valides pour les composites testés et pour des vitesses jusqu’ à 150 m/min.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.866
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.008
GPT teacher head0.191
Teacher spread0.183 · 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 designSimulation or modeling
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
Published2006
Admission routes2
Has abstractyes

Explore more

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