Study on the Effect of Cutting Fluids on Machining Performance in Milling Operations.
Bibliographic record
Abstract
This research investigates the influence of different cutting fluid application strategies on the machining performance of AISI 4340 alloy steel during end-milling operations. The study compares dry machining, Flood Cooling (FC), and Minimum Quantity Lubrication (MQL) across variables such as tool wear, surface roughness (Ra), cutting temperature, and power consumption. Experimental results indicate that while flood cooling provides the most significant reduction in cutting temperature (approx.35% reduction compared to dry), MQL offers a superior balance between surface finish and environmental sustainability. Specifically, MQL reduced surface roughness by 22% compared to dry machining due to enhanced lubrication at the tool-chip interface and the formation of a more stable tribological film. The paper concludes that the transition toward MQL and advanced nanofluids is essential for high-performance, sustainable manufacturing in the Industry 4.0 era, offering a pathway to reduce the carbon footprint and operational costs of heavy industrial machining while improving tool longevity and workpiece integrity.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".