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Record W3214150967 · doi:10.1115/imece2001/med-23312

A Model for the Tool Temperature During Machining With a Rotary Tool

2001· article· en· W3214150967 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

VenueManufacturing engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsRotational speedRakeMachiningDiscretizationMechanical engineeringChipRake angleHeat transferMachine toolMaterials scienceComputer scienceRotational energyMechanicsEngineeringPhysicsMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Abstract In this paper a model is developed to analyze heat transfer and temperature distribution resulting during machining with rotary tools. The presented model is based on a finite-volume discretization approach applied to a general conservation of energy statement for the rotary tool and chip during machining. The tool rotational speed is modeled and its effect on the heat partitioning between the tool and the chip is investigated. The model is also used to examine the influence of tool speed on the radial temperature distribution on the tool rake face. A comparison between the predicted and previously measured temperature data shows good agreement. In general the results show that the tool-chip partitioning is influenced dramatically by increasing the tool rotational speed at low to moderate levels of tool speed. Also, there is an optimum tool rotational speed at which further increase in the tool rotational speed increases the average tool temperature.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.518
Threshold uncertainty score0.899

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 head0.184
Teacher spread0.179 · 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