Microstructural characteristics and wear behaviour of HVAF sprayed Ni-SiC composite coatings
Bibliographic record
Abstract
High-velocity air fuel (HVAF)-sprayed SiC coatings have been investigated as a potential approach to address challenges associated with their successful application through thermal spraying, since SiC can potentially serve as a sustainable replacement for WC in tribological hard coatings. Ni-P capped SiC feedstock was processed to fabricate composite coatings using HVAF, which is a relatively recent versatile and cost-effective spray technique. Three coatings, Ni-SiC-1, Ni-SiC-2, and Ni-SiC-3 were sprayed using various parameters to investigate the relationship between the processing conditions, microstructure, and wear performance. The extent of SiC retained in each of the coatings, albeit fragmented, was determined to be ∼25 % for each of the three coatings compared to the 54 wt. % SiC presence in the feedstock. However, despite the similarity in the quantity of SiC retained in the respective coatings, two of the coatings (Ni-SiC-1 and Ni-SiC-2) showed mixed amorphous and crystalline phases, whereas the Ni-SiC-3 coating was fully crystalline. The microindentation hardness of the coatings in the as-sprayed and annealed forms was noted to be in the range of 800–850 HV0.1. The specific wear rates for the respective coatings in the as-sprayed and annealed forms were found to be promising but of the same order of magnitude, revealing no significant role of annealing on their tribological behaviour. The results establish that it is possible to process a suitably designed SiC feedstock using HVAF to realize a sustainable wear-resistant coating.
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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".