Effect of Microstructure on Wear and Corrosion Performance of Thermally Sprayed AlCoCrFeMo High‐Entropy Alloy Coatings
Bibliographic record
Abstract
High entropy alloys (HEAs) represent a new class of advanced metallic alloys that exhibit unique structural features and promising properties. The potential benefit of HEAs, in conjunction with established thermal spray manufacturing technologies, can provide a practical approach to mitigate wear and corrosion. Equiatomic AlCoCrFeMo HEA were fabricated using cold‐spraying and flame‐spraying, aiming to investigate the effect of low‐temperature and high‐temperature responses to phase formations, microstructural evolution, and microhardness. The performance evaluation during abrasive damage and electrochemical corrosion were also investigated. Microstructural studies revealed that coatings with body‐centered cubic (BCC) phases, where oxides were found in the flame‐sprayed coatings during in‐flight deposition. Hardness of the flame‐sprayed coatings showed noticeably (5.78 ± 0.45 GPa) higher than to that of the cold‐sprayed coatings (3.6 ± 0.48 GPa). Lower wear rates were achieved for the flame‐sprayed coatings (compared to the cold‐sprayed coatings. Oxide formations in the flame‐sprayed coatings decreased its corrosion performance such that it was two times lower than that of cold‐sprayed coatings. The results show that the microstructural features of flame‐sprayed coatings, coupled with formation of oxide inclusions resulted in improved resistance to damage due to wear loading, but undermined resistance to electrochemical degradation.
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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".