Manipulate A2/B2 Structures in AlCrFexNi Alloys for Improved Mechanical Properties and Wear Resistance
Bibliographic record
Abstract
Precipitation strengthening of body-center cubic (A2) alloys via ordered B2 nanoprecipitates is expected to achieve a desirable combination of strength and ductility. In this work, the A2/B2 configuration is manipulated by adjusting Fe content in medium-entropy AlCrFexNi (x = 0, 0.5, 1.0, 1.5, 2.0, 2.5 and 3.0) alloys fabricated via arc-melting for improved mechanical properties and wear resistance. As Fe content increases, the fraction of A2 phase increases, and A2 nanoprecipitates in the B2 matrix change to a weave-like A2/B2 structure. Continuously increasing Fe content leads to a mixture of BMAP (B2 matrix with A2 precipitates) and AMBP (A2 matrix with B2 precipitates), and finally to a complete AMBP structure. The yield strength decreases and fracture strain increases with increasing Fe content except x = 0. The alloy of x = 0 displays slightly higher hardness because of its relatively brittle B2 matrix. Cracks tend to propagate along A2/B2 interfaces. AMBP structure exhibits greater toughness than the BMAP structure. The alloy of x = 0 displays the second-greatest wear volume loss due to its relatively brittle B2 matrix. When Fe is added, the wear volume loss decreases considerably but shows a trend of an upward parabola with respect to the Fe content. After achieving the highest volume loss at x = 1.5 with a mixture of AMBP and BMAP, the volume loss decreases again. A completely uniform AMBP structure at x = 3.0 shows the least volume loss.
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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".