Enhancing microstructure and mechanical properties of FeNi1.5CrCu0.5 high-entropy alloy through precipitation treatment and cold rolling
Bibliographic record
Abstract
This study investigates how precipitation treatment affects the microstructure and mechanical properties of FeNi1.5CrCu0.5 high-entropy alloy, focusing on the role of precipitates during deformation. Homogenization followed by precipitation treatment formed Cr-rich precipitates, enhancing mechanical properties. After 80% cold rolling, the homogenized sample (HR) developed uniform fine shear bands, while the homogenized and precipitated sample (HGR) showed a heterogeneous distribution of fine and coarse shear bands. In the HGR sample, rotational dynamic recrystallization within shear bands produced new strain-free grains during deformation. Mechanical testing indicated that precipitation treatment increased ultimate shear strength from 459 MPa to 488 MPa, the shear yield strength from 340 MPa to 347 MPa, and the Vickers hardness from 134 HV to 171 HV, due to Cr 23 C 6 precipitates impeding dislocation motion. Following 80% cold rolling, the HGR sample exhibited slightly lower strength (ultimate shear strength: 526 MPa; shear yield strength: 353 MPa) compared to the HR sample. However, a significantly improvement in ductility observed, with shear elongation increasing from 10% to 22%, driven by strain-free grain formation. These results emphasize the critical role of precipitation treatments and cold deformation in optimizing the microstructure and mechanical properties of high entropy alloys for advanced engineering applications.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".