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Record W195820157

Étude d'un procédé de fraisage immergé applicable à une fraiseuse submersible et portable.

2011· article· fr· W195820157 on OpenAlexaboutno aff
Jean-Philippe Dextraze

Bibliographic record

VenuePolyPublie (École Polytechnique de Montréal) · 2011
Typearticle
Languagefr
FieldEngineering
TopicMaterials Engineering and Processing
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhysicsArt
DOInot available

Abstract

fetched live from OpenAlex

RESUME L'institut de recherche d'Hydro-Quebec s'efforce de developper une fraiseuse portable et submersible dans le but de realiser, en milieu submerge, des reparations sur des composantes de barrages hydro-electriques. Comme le fraisage en milieu immergee est tres peu documente, il a ete necessaire de realiser des essais dans le but d'analyser le procede a l'etat immerge et de le comparer a du fraisage a sec. Les experimentations ont porte sur l'etude de la vitesse et du mode d'usure des outils de coupe constitues de pastilles de carbures, sur la puissance requise par la broche et sur les proprietes des finis de surface obtenues lors de l'usinage d'acier ANSI1020. Lors des essais, nous avons constate que : - L'usinage sous l'eau genere une usure des outils beaucoup plus rapide en reduisant la duree de vie des pastilles de 91%. - Il existe une tres bonne correlation entre la puissance de coupe et le niveau d'usure des outils. Cette information permet de connaitre l'etat d'endommagement des outils de coupe sans avoir besoin de directement les mesurer. - Le milieu de coupe influence la puissance requise a la broche. Une plus grande puissance est necessaire lors de l'usinage sous l'eau, et ce, meme en faisant abstraction de la puissance dissipee dans le fluide due a la rotation de l'outil. - Le milieu de coupe ne semble pas affecter la rugosite du fini de surface (Ra). - Les finis de surface usines a sec montrent un profil beaucoup plus periodique que ceux usines dans l'eau. - Les parametres des finis de surface (kurtosis et skewness) sont tres constants a sec tandis qu'ils varient en fonction des parametres de coupe sous l'eau. A basse puissance de coupe, les surfaces usinees immergees ont des parametres forts differents des surfaces usinees a sec. Par contre, cette difference disparait a plus haute puissance de coupe.----------ABSTRACT Research Institute of Hydro-Quebec strives to develop a portable submersible milling machine in order to carry out, repairs on components of hydroelectric dams in submerged environments. As milling in submerged environments is very poorly documented, it was necessary to perform tests in order to analyze the process when immersed and compare it to dry milling. The experiments were carried on the rate and type cutting tools wear, on the required power by the spindle and on the surface finish properties while milling ANSI1020 steel. During testing, we found that: - Underwater milling greatly increase tool wear by reducing insert life by 91%. - There is a very good correlation between the cutting power and the level of tool wear. This information helps to know the state of damage on cutting tools without having to directly measure it. - The environment in influences the cutting power needed on spindle. More power is required when machining under water, even disregarding the power dissipated in the fluid due to tool rotation. - The milling environment does not seem to aaffect roughness (Ra) of surface finish. - The dry milled surfaces show a finish much more periodic than under water milled surfaces. - The surface finish parameters (skewness and kurtosis) are very consistent dry as they vary depending on cutting parameters under water. At high cutting power, these parameters on dry milled surfaces and under water milled surfaces are similar but quite different at lower cutting power.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.013
GPT teacher head0.214
Teacher spread0.201 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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