Manipulate A2/B2 Structures in AlCrFexNi Alloys for Improved Mechanical Properties and Wear Resistance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it