An Overview of Metallic Abradable Coatings in Gas Turbine Engines
Bibliographic record
Abstract
This review presents a comprehensive overview of metallic abradable coatings and the advanced testing methodologies used to evaluate their performance in gas turbine engines. Abradable materials are engineered to act as sacrificial coatings, enabling minimal blade tip wear while maintaining tight clearances between rotating blades and stationary components. Such functionality is critical in aerospace applications, where engines operate at high rotational speeds and across wide temperature ranges. The review examines the principal factors governing the design and selection of metallic-based abradable coatings, including material composition, thermal stability, and microstructural tailoring through the addition of phase modifiers, porosity formers, and solid lubricants. The performance of various metallic matrix materials is also discussed concerning their operational temperature ranges and wear characteristics. Particular attention is given to abradability evaluation methods, emphasizing the need to replicate engine-representative conditions to capture blade–coating interactions, frictional behavior, and wear mechanisms. This review consolidates advances in material compositions, microstructural engineering, and experimental testing, integrating perspectives from materials science, tribology, and methodology to guide the development of next-generation turbine coatings. It specifically addresses the lack of a unified review linking material design, thermal spray processes, and performance evaluation by summarizing key compositions, microstructures, and testing methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".