A sustainable route to manufacture refractory high entropy alloy of AlMoNbTaTiZr from metal powder produced in solid state
Bibliographic record
Abstract
To enhance efficiency and reduce CO2 emissions in applications such as jet-engines, gas turbines, and nuclear powerplants, alloys that withstand high temperatures are essential. High entropy alloys (HEA) containing refractory elements offer superior high-temperature properties. One of these refractory high entropy alloys (RHEAs) is AlMo0.5NbTa0.5TiZr. There are challenges when manufacturing these compositionally complex alloys using conventional techniques since they have elements with very high (Nb, Ta, Mo), high (Zr, Ti) and lower (Al) melting temperatures, creating mixing and homogeneity issues in alloy preparation. The Fray-Farthing-Chen (FFC) Cambridge process directly creates RHEA’s powders without melting, but these feedstocks require a suitable consolidation technique. In this work field-assisted sintering technique (FAST), a novel rapid sintering technique, was used to make parts from powder of this alloy produced in solid-state.Among the process parameters the consolidation temperature had a more profound effect on density. From studied temperatures 1400 °C with a dwell time of 15 minutes produced the highest density level. Such a manufacturing route, occurring at temperatures lower than traditional casting, increases sustainability, and produces a homogeneous microstructure leading to parts with uniform properties and enhanced in-service performance.
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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 | 0.001 |
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".