Adjusting (AlNi)/(FeCr) ratio to tailor microstructure and properties of A2-B2 dual-phase (AlNi)x(FeCr)100-x medium-entropy alloys
Bibliographic record
Abstract
Metallic materials composed of alternating soft and hard phases can be tailored for desirable strength-ductility combinations. In this work, (AlNi) x (FeCr) 100-x (x = 40, 50 and 60) A2-B2 dual-phase medium-entropy alloys (MEAs) fabricated using an arc-melting furnace were studied. Fractions and morphologies of (Fe, Cr)-rich A2 and (Al, Ni)-rich B2 phases in the alloys were modified by simply adjusting the ratio of (AlNi) to (FeCr), based on phase diagrams calculated using Thermo-Calc software. The (AlNi) x (FeCr) 100-x alloys showed different microstructural features, including interdendritic regions with irregular A2-B2 lamellae (in all three alloys), and dendrite cores with different morphologies such as A2 matrix embedded with B2 particles (x = 40), A2-B2 weave-like structure (x = 50), and B2 matrix embedded with A2 nanoparticles (x = 60). Based on micro-indentation tests, all the core zones showed higher hardness than interdendritic regions, benefiting from their weave-like or particle-dispersed microstructure. Compressive tests and EBSD analyses indicated that the presence of the core zone having a structure of B2 matrix embedded with A2 nanoparticles was particularly effective for enhancing the strain-hardening capacity and wear resistance. This study demonstrates a simple way, via directly adjusting the fraction ratio of (AlNi) to (FeCr), to control the heterogeneous structure of this MEA system for desirable properties, which can be extended to other A2-B2 dual-phase multi-principal element alloy systems.
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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".