Erosion Behavior of WC-10Co-4Cr HVOF Coatings
Bibliographic record
Abstract
Abstract Among the WC based cermets coating materials, those using the 10%Co-4%Cr matrix demonstrate excellent wear and corrosion properties. In this paper the erosion behaviors of WC-10%Co-4%Cr HVOF coatings was evaluated under different erosion conditions. The coatings were obtained from the JP-5000 gun using kerosene as fuel and the Diamond Jet gun using propylene and hydrogen. Two types of powder morphology were used: the first type was coarse and angular while the second was smaller, porous and spherical. The coatings were submitted to dry and slurry erosion. Erosion tests were performed at room temperature for both dry and slurry erosion. Dry erosion was evaluated by jet erosion while for slurry erosion, two different tests were performed: Coriolis and jet impingement erosion. Experimental results show that for the same powder, the erosion resistance measured by the Coriolis test exhibits 100% variation while for jet impingement it varies only by 20% depending on gun and spray conditions. The results also show that the powder selection is one of the key factors controlling the coating performance. The selection of powder can affect the Coriolis erosion resistance by more than 300% and the dry and slurry erosion resistance by 75%. The results are analyzed as a function of the processing and coating microstructure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".