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Record W2344521202 · doi:10.5267/j.esm.2016.3.001

Influence of machining parameters on surface roughness and dry friction

2016· article· en· W2344521202 on OpenAlexvenueno aff
N. Satheesh Kumar, Punit Kumar

Bibliographic record

VenueEngineering Solid Mechanics · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMachiningSurface roughnessMaterials scienceSurface finishMetallurgyDry frictionMechanical engineeringComposite materialEngineering

Abstract

fetched live from OpenAlex

Dry friction depends on the surface topography which, in turn, is governed by machining parameters in addition to several other factors,. Therefore, in order to establish a qualitative relationship among these factors, the surface roughness and coefficient of static friction are measured for specimens machined on lathe and shaper machines with different values of machining parameters. For the case of lathe, the measured Ra value is found to increase with increase in feed rate and depth of cut, whereas, a marginal decrease is observed with increasing spindle speed. Similar results with respect to cutting speed have been obtained for the case of shaper machine. On the other hand, the coefficient of static friction, measured on steel substrate using inclined plane method, is found to decrease with increasing Ra values for both the specimen types.

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: none
Teacher disagreement score0.518
Threshold uncertainty score0.497

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 head0.005
GPT teacher head0.206
Teacher spread0.201 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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