Minimal quantity cooling lubrication in turning of Ti6Al4V: Influence on surface roughness, cutting force and tool wear
Bibliographic record
Abstract
Titanium alloys generally show low machinability ratings. They are referred as difficult-to-cut materials due to their inherent properties such as low thermal conductivity, high chemical reactivity and high strength at elevated temperatures. Cooling strategies play an important role to improve the machining performance of the cutting process. In order to facilitate the heat dissipation from the cutting zone, generous amount of coolant is used when machining highly reactive metals such as titanium alloys. Generally, cutting coolants are nominated as pollutants due to their non-biodegradable nature. This article presents experimental evaluation of a minimal quantity cooling lubrication system. The study investigates a combination of sub-zero-temperature air and vegetable oil–based mist as possible environmentally benign alternative to conventional cooling methods. The results are compared with the dry and flood cutting environments as well. Machinability was evaluated experimentally by considering the surface finish, cutting forces, tool life and their associated tool wear mechanisms. It was concluded from the results obtained from the surface roughness, cutting force and tool life investigation that minimal quantity cooling lubrication (internal) cooling strategy has encouraging potential to replace the conventional flood cooling method.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".