Influence of Composition and Processing Methods on the Microstructure and Properties of Co-Cr-Fe-Mn-Ni High Entropy Alloys
Bibliographic record
Abstract
This research was undertaken to evaluate potential use of high entropy alloys (HEAs) for high-temperature gas turbine applications.The microstructure and properties of three HEAs -CoCrFeMnNi, Al0.5CoCrFeMnNi1.5, and Al0.5CoCrFeNi1.5 -were examined to assess the impact of Mn and Al additions and the effect of heat treatments on the microstructure and the mechanical properties.In addition, preliminary process development of manufacturing one of the alloys using a laser based additive manufacturing (AM) method was also conducted.The results showed that Al addition resulted in hardness increase due to the formation of FCC-L12 Ni3Al and BCC-B2 NiAl phases, while Mn addition enhanced the hardness at the expense of lowering phase stability temperature and solidus temperature.The AM process development, based on a laser powder direct energy deposition (LP-DED) system, showed that laser power, scanning speed, and material feed rate all had significant impact on resulted sample height and width.Finally, the comparative analyses of CoCrFeMnNi HEAs, processed through casting (hot isostatic pressed in the as-received condition), hot isostatic pressing (HIP) of powder material, and LP-DED, revealed that sintering by HIP resulted in superior density and reduced defects in the material.Tensile testing of LP-DED-fabricated CoCrFeMnNi revealed minimal differences in strength along two deposition orientations, while reduced ductility was observed along the transverse orientation.This study concluded that the alloy composition, manufacturing techniques, and heat treatment all resulted in microstructure changes, hence subsequent impact on the 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 | 0.000 |
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".