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Record W2810945665 · doi:10.1139/tcsme-2017-1009

PREDICTION AND INVESTIGATION OF SURFACE ROUGHNESS WHILE TURNING SG IRON WITH CUBIC BORON NITRIDE (CBN) AND TUNGSTEN CARBIDE INSERTS

2017· article· en· W2810945665 on OpenAlexvenueno aff
K.M. Kumar, P. Hariharan

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceBoron nitrideTungsten carbideSurface roughnessMetallurgyCast ironCarbideGraphiteDrillingTinSurface finishComposite material

Abstract

fetched live from OpenAlex

This work compares the effect of cubic boron nitride (CBN) and multilayer (TiCN+Al 2 O 3 +TiN) coated tungsten carbide (WC) tools during the turning of spheroidal graphite (SG) nodular iron. Nodular irons have more ductility which is required in mechanical components that demand high fatigue resistance like crankshafts, cam shafts, bearing caps and clutch housings. The impact of various process parameters like the depth of cut, cutting speed and feed on the surface roughness (R a ) of SG iron is studied and optimized using the response surface model. The chip morphology is also discussed for evaluation of the quality of the turned surface. The experimental outcomes reveal that the WC tool offers a high surface finish at the optimal combination of the cutting speed at 102 meter/minute, feed at 0.051 millimeter/revolution and depth of cut at 0.5 millimeter and that, for the CBN insert, at 245 meter/minute of cutting speed, 0.051 millimeter/revolution of feed and 0.75 millimeter of depth of cut.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.509

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 head0.010
GPT teacher head0.188
Teacher spread0.178 · 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 designSimulation or modeling
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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

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