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.
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.
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".