An Evaluation of the Tribological Behavior of Cutting Fluid Additives on Aluminum-Manganese Alloys
Bibliographic record
Abstract
The introduction of additives enhances the friction and wear reduction properties of cutting fluids (CFs) as well as aids in improving the surface quality of the machined parts. This study examines the tribological behavior of polymer-based and phosphorus-based additives introduced into cutting fluids for the machining of Al-Mn alloys. Ball-on-disc tests were used to evaluate the coefficient of friction (COF) and lubrication failure temperature to study the performance of the additives in the cutting fluids. Surface characterization was performed on the sliding tracks induced on the Al-Mn disc surfaces and used to propose the wear and friction reduction mechanisms. The polymer-based additive possessed a higher temperature at which lubrication failure occurred, displayed comparable COF at a lower temperature under certain conditions, and possessed a steadier tribological behavior. However, the phosphorus-based additive was observed to display lower COF and wear damage from 200 °C till failure. The lower COF values for the phosphorus-based additive at 200 °C corresponded with lower surface damage on the Al-Mn surface. The phosphorus-based additive’s performance at 200 °C could be attributed to the forming of a phosphorus-rich boundary layer within the sliding wear track, resulting in less surface damage on the Al-Mn surface and lower material transfer to the counterface steel ball surface.
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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) | 0.001 | 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".