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Record W2293772896 · doi:10.1139/tcsme-2008-0008

COMPARISON OF CONVENTIONAL, COHERENT-JET AND HIGH-PRESSURE COOLANT DELIVERY SYSTEMS FOR PROFILE GRINDING

2008· article· en· W2293772896 on OpenAlexaffvenue
Andrew Warkentin, Robert Bauer, Don Hartlen

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsDalhousie University
Fundersnot available
KeywordsCoolantGrindingJet (fluid)Mechanical engineeringMaterials scienceVolumetric flow rateMechanicsFlow (mathematics)PhysicsEngineering

Abstract

fetched live from OpenAlex

In this work, conventional (non-coherent), coherent-jet, and high pressure coolant delivery systems are compared for profile creep-feed grinding, under non-continuous dressing conditions. To facilitate this comparison, an analytical method of determining the available flow (coolant that hits the wheel or workpiece) was presented. The coolant delivery systems were compared for five different feed rates. At each feed rate the form error was measured using a coordinate measuring machine at five cross sections along the workpiece. The high-pressure coolant delivery system had the lowest form error and had an available flow of only 1.7 L/min. The non-coherent jet had the second best form error and had an available flow of 17 L/min. The coherent jet had the highest form error with an available flow of 39 L/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 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)0.002
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.230
Teacher spread0.212 · 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".

Quick stats

Citations5
Published2008
Admission routes2
Has abstractyes

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