INFLUENCE OF THE ANNEALING ON THE WEAR RESISTANCE OF GAS-THERMAL COATINGS FROM Ni-Cr-Al PSEUDOALLOY
Bibliographic record
Abstract
The structural-phase state and tribotechnical properties of gas-thermal coatings made of Ni-Cr-Al pseudoalloy are investigated in the initial state and after annealing in the temperature range of 550-650 °С with holding time of 20–60 min. It is shown that in coatings sprayed by the method of high-speed metallization of wires made of Х20Н80 (Kh20N80) and aluminum АД-1 (AD-1), the phase composition includes γ-(Ni, Cr, Fe), Al and Al2O3. It is found that high-temperature annealing of Ni-Cr-Al coatings leads to the precipitation of intermetallic compounds Al3Ni, Ni2Al3, Ni3Al, and NiAl in them, as well as to an increase in the porosity of the coatings up to ≈15–20 vol.%, which is associated with the implementation of the Frenkel and Kirkendall effects. Tribotechnical tests of coatings are carried out according to the scheme of the reciprocating movement of the sample along the plate counterbody in the dry friction mode at a load of 1.5 MPa. It is shown that as a result of the annealing of the coatings, an increase in their wear resistance under dry friction conditions is registered up to 24 times in comparison with the initial state. In particular, the intensity of mass wear of the Ni-Cr-Al coating in the initial state is 28.7 × 10–3 mg/m, and those subjected to annealing at 600 °C for 60 min — 1.2 × 10–3 mg/m. Based on the carried out multifactorial experiment, it is found that the maximum wear resistance of Ni-Cr-Al-pseudo-alloy coatings under dry friction conditions is achieved as a result of their annealing at temperatures of 630–640 °С and a holding time of 40–50 min, which is associated with the release of large number of dispersed intermetallic phases Ni3Al and NiAl with a relatively insignificant increase in the porosity of the coatings.
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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".