Effect of WC powder morphology on dry sliding wear behavior of cold sprayed CrMnCoFeNi cantor HEA composite coatings at room temperature
Bibliographic record
Abstract
The Cantor alloy's excellent ductility and toughness have made it a popular thermal spray feedstock material, however, its wear resistance is limited. In this work, in an effort to produce cold spray Cantor coatings with further improved tribological performance, WC particles of different morphologies (agglomerated and cast) were added to a gas atomized spherical Cantor alloy powder feedstock to deposit metal matrix composite (MMC) coatings via high pressure cold spray (CS) and laser-assisted cold spray (LACS) methods. The process-microstructure-property relationships of the coatings were investigated and compared. Reciprocating sliding wear tests were performed in ambient conditions on the coatings with WC/Co counterface spheres. The addition of different morphologies of hard ceramic phases to the Cantor alloy matrix had varying effects on the wear rate, and on the friction and wear behavior of the CS coatings. Indeed, the MMC coatings containing WC agglomerated particles presented a higher wear rate than the Cantor CS reference coating, namely due to the low intrinsic cohesion strength of the agglomerated particles. In turn, agglomerated particle decohesion caused fine hard WC particle dispersion on the wear track and severe abrasive wear. The spherical cast WC particle-containing MMC coatings, however, presented a reduced wear rate due to the formation of a stable oxide tribolayer. The LACS coatings presented an increased cohesion strength compared to their CS counterparts with slightly improved wear rates.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".