Effects of rare-earth element yttrium (Y) on the solid-particle erosion resistance of AlCrFeNi medium-entropy alloy at ambient and elevated temperatures
Bibliographic record
Abstract
High-entropy alloys (HEAs) and medium-entropy alloys (MEAs) have demonstrated many superior properties, including high strength, toughness, and thermal stability. Among MEAs, AlCrFeNi has received considerable attention due to its well-balanced mechanical properties and cost-effectiveness, making it a promising candidate for industrial applications. It is known that rare-earth elements can effectively enhance the oxidation resistance of alloys containing passive elements. In this study, AlCrFeNi MEAs with Y additions (0.5, 1.0, and 1.5 wt.%) were fabricated by arc melting. Microstructures of fabricated samples and their mechanical properties and resistance to air-jet sand erosion were investigated at both room temperature (20°C) and elevated temperature (700°C). For the sake of comparison, a commercial nickel-based Waspaloy superalloy was also evaluated. At both room and elevated temperatures, the AlCrFeNi MEA demonstrated high superiority over the widely used nickel-based superalloy. The Y additions increased the hardness and yield strength of the MEA, leading to improved erosion resistance at room temperature, which was more obvious at elevated temperatures, largely benefiting from the improved resistance to accelerated oxidation at elevated temperatures. The obtained information highlighted the beneficial effects of the minor Y additions in enhancing both the mechanical performance and high-temperature durability of the AlCrFeNi MEA, which would help extend the application of the MEA to higher temperatures.
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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".