Implementation of Sustainable Vegetable-Oil-Based Minimum Quantity Cooling Lubrication (MQCL) Machining of Titanium Alloy with Coated Tools
Bibliographic record
Abstract
The lubrication capacity and penetration ability of the minimum quantity cooling lubrication-based strategy is linked with lubrication specific parameters (oil flow rates and air pressure), cutting conditions, and chip formation. It points out the complex selection involved in the MQCL-assisted strategy to attain optimal machining performance. Lubrication during metal cutting operations is a complex phenomenon, as it is a strong function of the cutting conditions. In addition, it also depends on the physical properties of the lubricant and chemical interactions. Minimum Quantity Lubrication (MQL) has been criticized due to the absence of cooling parts; MQCL is a modified version where a cooling part in the form of sub-zero temperatures is provided. The aim of this paper was to investigate the influence of different lubrication flow parameters under minimum quantity cooling lubrication (MQCL) when machining aeronautic titanium alloy (Ti6Al4V) using Titanium Aluminum Nitride—Physical Vapor Deposition (TiAlN-PVD) coated cutting inserts. The machining experiments on the MQCL system were performed with different levels of oil flow rates (70, 90, and 100 mL/h) and the performance was compared with the conventional dry cutting and flood cooling settings. A generic trend was observed that increasing the oil flow rate from 70—mL/h to 100 h/h improved the surface finish and reduced thermal softening at a low feed of 0.1 mm/rev. The results revealed that many tool-wear mechanisms such as adhesion, micro-abrasion, edge chipping, notch wear, built-up edge (BUE), and built-up layer (BUL) existed.
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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 | 0.000 |
| 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 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".