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Record W1997649178 · doi:10.1504/ijmms.2014.067164

Characterisation and optimisation of minimum quantity lubrication in milling of Ti-6Al-4V alloy using phase Doppler anemometry (PDA)

2014· article· en· W1997649178 on OpenAlexafffund
Amit Tendolkar, A. Damir, Helmi Attia, Patrick Hendrick, Christophe Diakodimitris

Bibliographic record

VenueInternational Journal of Mechatronics and Manufacturing Systems · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsNational Research Council CanadaMcGill University
FundersMitacs
KeywordsNozzleMachiningLubricationMaterials scienceVolumetric flow rateSurface roughnessMechanical engineeringFlow visualizationSpray characteristicsSurface finishFlow (mathematics)MetallurgySpray nozzleEngineering drawingComposite materialMechanicsEngineering

Abstract

fetched live from OpenAlex

The main objective of this work is to understand the effect of the MQL parameters; namely, oil flow rate, air flow rate and nozzle distance from the cutting zone, on the flow characteristics in order to optimise the cooling and lubrication capacities of the jet for machining applications. Flow visualisation experiments were performed for different air and oil flow rates and distances from the nozzle using phase Doppler anemometry (PDA). The visualisation results, such as, the droplet size and velocity vector were used to identify the optimum MQL conditions to achieve the desired flow characteristics for machining applications. It was found that a spray with high air flow rate and high oil flow rate would give an axial, symmetrical, coherent, and undisturbed spray, which is characterised by small droplet size and high velocity. This spray is optimum for machining due to its ability for better penetration and cooling effect in the cutting zone. Milling tests were performed on Ti-6Al-4V alloy to validate the effect of MQL parameters on the machining performance, in terms of cutting forces, surface roughness and tool temperature.

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 score0.227
Threshold uncertainty score0.395

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 head0.012
GPT teacher head0.262
Teacher spread0.250 · 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

Citations6
Published2014
Admission routes2
Has abstractyes

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