Influence of V concentration in TiAlSiVN coating on self-lubrication, friction and tool wear during two-pass dry turning of austenitic steel 316 L
Bibliographic record
Abstract
The present work investigates the performance of TiAlSiVN coating with 5 and 11 at% of V concentration deposited on the Al2O3/SiC cutting tools during dry turning of austenitic 316 L stainless steel. The maximum flank wear reduction compared to the uncoated tool for coated tools with 11% and 5% V concentration was 85% and 67%, respectively. The Raman analysis indicated the formation of V2O5 in the cutting zone, which helps to reduce friction and machining forces for the coated tools. Overall, the presence of higher V content (11 at.%) enhances the self-lubrication behaviour of the TiAlSiVN coating, accounting to lower fluctuations in cutting forces, superior surface finish, and lower flank wear when compared to the TiAlSiV5N coated and uncoated cutting tools.
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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".