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Record W2101822910 · doi:10.1115/imece2004-60463

Thermal Consideration of the Design of Multilayer Coated Tools for High Speed Machining

2004· article· en· W2101822910 on OpenAlexafffund
Helmi Attia, L. Kops

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMachiningThermal contact conductanceCoatingContact areaThermalContact resistanceAsperity (geotechnical engineering)Composite materialThermal resistanceMechanical engineeringLayer (electronics)MetallurgyEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Quantitative assessment of the thermal role of multi-layer coating in cutting tools was approached through the analysis of mechanical contact problem at the tool–chip interface and the constriction resistance phenomenon. The micro-contact configuration on the surface asperity level (size and density of contact points, and surface approach) and the macro-contact configuration (contact pressure distribution and the size of the adhesion and sliding zones) were defined. The effect of multi-layer coating on stiffness of the contact interface was experimentally investigated and used to estimate effective flow stress of contacting solids. Thermal constriction model, based on the concept of heat flow channel, was developed. Using FE simulation, the correlation between the contact pressure and the thermal contact resistance of uncoated and multi-layer coated tools were established and validated. The thermal interaction and heat redistribution in the workpiece–chip-tool system was then examined for multi-layer coated tools in conventional and high speed machining. Analysis of the results showed that the tool coating causes the reduction of the heat flowing into the tool and the reduction of the maximum temperature rise. The thermal constriction model developed in this work provides a methodology for the design of coated tools based on thermal considerations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.196

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 head0.040
GPT teacher head0.247
Teacher spread0.207 · 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.

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

Citations3
Published2004
Admission routes2
Has abstractyes

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