Study of tribofilms generation at different cutting speeds in dry machining hardened AISI T1 and AISI D2 steel
Bibliographic record
Abstract
Hard dry machining of AISI T1 and AISI D2 steels at comparable hardness (around 59 HRC) were performed at 50 m/min, 80 m/min and 100 m/min cutting speeds separately with uncoated alumina ceramic tool inserts (Al 2 O 3 + TiC), in order to study the effect of cutting conditions on the generation of tribofilms. Comprehensive assessment of uncoated ceramic inserts in machining of T1 and D2 at different cutting speeds was made by optical microscope, scanning electron microscope and X-ray photoelectron spectroscopy. It was shown that higher cutting speeds increased the formation of thermal protective and/or lubricating tribofilms. In machining D2 steel, more intensive formation of Cr-O tribo-oxides brought a lower wear rate; while in cutting T1 steel, a higher amount of W-O tribofilms generation provided better lubrication. At higher cutting speeds, wear/friction behaviour changes were attributed to differences in two factors: first, from a traditional micro-scale standpoint, the distribution of carbide within steels and the accumulation of thermal and mechanical damages during different cutting conditions; second, from a new nano-scale viewpoint, the generation of protective/lubricating tribofilms due to change of machining conditions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".