Characterisation and optimisation of minimum quantity lubrication in milling of Ti-6Al-4V alloy using phase Doppler anemometry (PDA)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".