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Record W2315242785 · doi:10.1504/ijmmm.2016.075469

Experimental investigation on part quality and metallic particle emission when milling 6061-T6 aluminium alloy

2016· article· en· W2315242785 on OpenAlexafffund
Seyed Ali Niknam, Jules Kouam, Victor Songméné

Bibliographic record

VenueInternational Journal of Machining and Machinability of Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMachiningMaterials scienceMetallurgyAluminiumAlloyParticle sizeAluminium alloyCoatingParticle (ecology)Insert (composites)Quality (philosophy)RADIUSSurface roughnessComposite materialEngineering

Abstract

fetched live from OpenAlex

The quality of machining operations has a direct relationship with machined part quality and workshop air quality. The main characteristics that describe the machined part quality are burr size and surface finish, while fine particles are also considered as air quality characteristics in machining operations. In the present study, four main surface finish parameters (Ra, Rt, Rz and Rq), burrs size and mass concentration of metallic particles in milling of 6061-T6 aluminium alloy are investigated. The machining factors considered to build the experimental plan are cutting tool coating, insert nose radius, cutting speed and feed per tooth. The statistically significant responses to variation of process parameters and factors governing them are presented.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.034
GPT teacher head0.300
Teacher spread0.266 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

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