Evaluation of Tool Wear Suppressive Ability of Lubricants Usein in Minimum Quantity Lubrication Application in High Speed Machining of Cast Aluminum Alloys
Bibliographic record
Abstract
The disadvantages of conventional metalworking fluids such as disposal problems, health problems and economic factors have led to the development of strategies to reduce their amount in metalworking. Recently, Minimum Quantity Lubrication (MQL) technology was developed and it seems to be a suitable alternative for economically and environmentally compatible production. It combines the functionality of lubrication with an extremely low consumption of lubricant and has a potential to replace metalworking fluids application in machining operations. The MQL lubricants are formulated with two major groups of additives; anti-wear (AW) additives and extreme pressure (EP) additives. When such lubricants are applied to the cutting zone, protective layers are formed on the interacting surfaces of the workpiece and the cutting tool. These layers prevent direct contact between the tool and chip surfaces, and, therefore reduce friction forces and tool wear. In order to utilize MQL to its full potential, it is essential to select appropriate lubricant composition for particular work material and machining parameters. The experimental study of different compositions of MQL lubricants is reported. The effectiveness of the lubricants are determined in terms of their ability to protect the cutting tool in high speed machining of cast aluminum alloys, which are widely used in automotive industry. The main objective of this research is to quantitatively evaluate the ability of lubricant’s additive composition to reduce the tool wear. This is reached through the comparison between the tool wear rate measured during the machining of aluminum cast alloy with the application of MQL, and the tool wear rate obtained in dry machining of the same alloy. Two kinds of the lubricants are evaluated; vegetable and synthetic. The content of AW and EP additives in each kind of lubricant was varied on three levels in order to capture the effect of the lubricant’s composition on tool wear. The result of the MQL lubricants evaluation is discussed and the recommendations for optimal lubricant composition are made.
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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 | 0.001 | 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".