Influence of Slight Pressure Variations on the Mechanical and Tribological Properties of Ti/TiAlV/TiAlVN Coatings Produced by Magnetron Sputtering
Bibliographic record
Abstract
Ti/TiAlV/TiAlV(N) coatings were deposited by D.C magnetron sputtering technique. This experiment was carried out on 316L stainless steel substrates, and it was produced varying the working pressure, in an environment composed by a mixture of Ar/N2. While Ti and TiAlV layers were produced at a constant pressure, TiAlV(N) layers were grown at 0.6, 0.8, 0.9 and 1.0 Pa, for samples M1, M2, M3 and M4, respectively. After the production, morphological, compositional and structural properties of coatings were studied using techniques as x-ray diffraction (XRD), energy dispersive spectroscopy (EDS), and atomic force microscopy (AFM). Next, mechanical and tribological properties such as hardness, Young’s modulus, coefficient of friction, and adherence were evaluated, using nanoindentation, scratch test and ball on disc methods. XRD analysis evidenced that coatings crystallized in a salt rock FCC structure, and the crystallite size tended to increase with the increase of pressure. The XPS analysis allowed to demonstrate the presence of the doublets corresponding to metallic aluminum. Titanium was also found as well as the presence of oxygen as an oxidizing element of the coating. Regarding to the morphological analysis, it was observed an increase of the roughness when the coatings were grown, compared with the stainless substrates; moreover, no significant influence of the small pressure variation was observed on properties as hardness, coefficient of friction, and critical load.
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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 | 0.000 | 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".