Erosive wear properties of Ti–Si–N nanocomposite coatings studied by micro-sandblasting
Bibliographic record
Abstract
Superhard Ti–Si–N nanocomposite coatings were deposited on high speed steel substrates by a combined dc/rf unbalanced reactive magnetron sputtering process. Erosion tests using angular Al2O3 particles (mixture of cylindrical and trihedral particles) with an average particle size of 17μm were carried out using air-sand blasting to investigate the erosive wear properties of the coating. For benchmarking purpose, three other hard or superhard coatings, namely, TiN, nanolayered TiN/NbN and multiple-layer CrTiN/NbN have also been evaluated under the same experimental conditions. In the air pressure range used in this work from 1.3 to 4.2 bar, the nanocomposite Ti–Si–N coatings demonstrated extremely low steady-state erosion rates compared with the other three coatings evaluated. However, pinholes associated with the particle impacts were observed in the tests of Ti–Si–N coating, which propagated with the increase of air pressure or particle velocity and the particle impingement duration, and eventually led to coating failure. The erosive failure mechanism and the dependence of the erosion rate on coating properties and experimental conditions were discussed based on the microstructural analysis.
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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) | 0.001 | 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".