Effect of Mo and Cr on abradability and tribological performance of AlSi-based abradable coatings
Bibliographic record
Abstract
Abradable coatings have historically been utilized as sealing materials to enhance aerospace engine efficiency by minimizing the gap between stationary and rotating components in the low-pressure compressor stage. During potential blade-abradable interaction, the abradable material assumes a sacrificial role, thereby preventing the blade from structural damage. While extensive research has been devoted to developing corrosion-resistant abradable systems, ensuring their optimal abradability and tribological performance is crucial. Thus, this study aims to investigate two thermally sprayed aluminum-silicon-based (AlSi) coatings in terms of their tribological and abradability performance. While both coatings contain polyester, one incorporates a small concentration of molybdenum (Mo) and chromium (Cr). These coatings are named as AlSi-Poly and AlSi-MoCr, respectively. Both abradable coatings were investigated in terms of HR15Y hardness, microstructure, surface roughness, and wear. The wear performance of both coatings was evaluated through tribological studies, including ball-on-disk and ball-on-flat, and subsequently compared with abradability tests conducted using a custom-built abradable rig designed to closely simulate the blade-abradable interaction. The coating characterization revealed equivalent results for both abradable materials in terms of hardness, surface roughness, and filler material distribution. The tribological assessment showed an equivalent frictional coefficient and wear rates between the coatings, and no apparent damage to the Ti6Al4V counterballs was observed. However, slight differences in wear mechanisms were observed between the testing setups. The abradability evaluation of both coatings showed comparable wear mechanisms, including longitudinal grooves and smearing, with comparable wear track roughness, reaction forces and rubbing temperatures. Although no evident blade wear was observed, the blade used against AlSi-MoCr exhibited slightly more spread aluminum transfer. Overall, the consistent results across different TRL approaches, including tribological and abradability performance evaluations, support the equivalence of both coatings as effective abradable materials. The inclusion of Mo and Cr in the AlSi matrix showed minimal impact on wear performance, further validating their use in advanced applications.
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 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) | 0.000 | 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) | 0.000 | 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".