Heat treatment effect on wear behaviour of HVOF-sprayed near-nanostructured coatings
Bibliographic record
Abstract
This study investigates the effect of heat treatment on changes in microstructure and wear behaviour of WC-NiCr coatings. Two feedstock powders with a similar chemical composition and different particle sizes (near nano-structured WC-17NiCr and microstructured WC-15NiCr) were used. High-velocity oxyfuel spraying technique was used to deposit coatings on to a mild steel substrate using identical spraying parameters. Coated samples were then heat treated in a nitrogen atmosphere at 500 and 700°C. The effect of heat treatment on changes in hardness and wear performance of the coatings was studied using microstructural analysis, micro-hardness indentation and abrasive wear tests. The results showed that the heat treatment increased the hardness of both coatings and a corresponding increase in wear resistance was recorded. The formation of a brittle CrWO 4 phase in the microstructured coating resulted in brittle fracture of the coating and this gave lower wear resistance compared to the nanostructured 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 | 0.000 |
| 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".