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Record W2803133548 · doi:10.1139/tcsme-2017-1043

OPTIMIZATION OF HIGH SPEED CNC END MILLING PROCESS OF BSL 168 ALUMINIUM COMPOSITE FOR AERONAUTICAL APPLICATIONS

2017· article· en· W2803133548 on OpenAlexvenueno aff
R. Suresh Kumar, John S Alexis, Vinoth Thangarasu

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsAluminiumBox–Behnken designResponse surface methodologySurface roughnessComposite numberProcess (computing)Materials scienceDesign of experimentsMachiningMechanical engineeringEnd millingCentral composite designEngineering drawingProcess engineeringComputer scienceComposite materialEngineeringMetallurgyMathematicsStatisticsMachine learning

Abstract

fetched live from OpenAlex

This research paper carries out analyses to suggest a scientific model for machining of BSL 168-Aluminium composites by optimization of input parameters of CNC high-speed milling using Box Behnken based response surface method. A design of experiment (DOE) based Box Behnken array of experiments for 4 factors with 3 variations of 34 variations is developed and tested to find the responses. The received responses are developed into a mathematical model. The significance of the individual parameter is ascertained using desirability functions and confirmatory runs. The derived optimized set minimizes the surface roughness to a value of less than 2 microns and with 37% extra material removal rate and best in class geometrical quality.

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: Methods · Consensus signal: none
Teacher disagreement score0.784
Threshold uncertainty score0.505

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 head0.232
Teacher spread0.222 · 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
GenreMethods

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

Citations13
Published2017
Admission routes1
Has abstractyes

Explore more

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