Effect of WS <sub>2</sub> particles in cutting fluid on tribological behaviour of Ti–6Al–4V and on its machining performance
Bibliographic record
Abstract
Tribological behaviour of Ti–6Al–4V alloy sliding against WC-Co was evaluated by employing WS2 nanoparticles blended in a cutting fluid used for machining of this alloy. Pin-on disk tests were carried out under boundary-lubricated condition using a cutting fluid (70% water and 30% oil) blended with WS2 nanoparticles (CF + WS2). When a cutting fluid with ≥ 0.5 wt.% WS2 was used, the COF of the tribosystem was reduced compared to CF + 0%WS2. The lowest COF of 0.05 was obtained when 1.0 wt.% WS2 was used. Low and stable COF values were accompanied by the formation of a tribolayer incorporating WS2 and WO3 on the WC-Co surfaces. During orthogonal machining of Ti–6Al–4V using CF + 1.0%WS2, a tribolayer with the similar composition was formed on the cutting edge of the WC-Co tool and the average cutting force was reduced by 35% compared to cutting with CF + 0%WS2. Machining with CF + 1.0%WS2 produced thinner chips. Other improvements in machining performance attained using CF + 1.0%WS2 included reduction of adhesive wear on the tool and a lower roughness of the machined 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".