Tune Al/Ti to adjust FCC+L21 hetero-structured Ni-based high-entropy alloys for improved mechanical properties and wear resistance
Bibliographic record
Abstract
Outstanding mechanical properties of Ni-based superalloy benefit from its coherent γ/γ’ structure via precipitation strengthening of γ matrix (FCC structure) by L1 2 Ni 3 Al-type γ’ phase. Back-stress strengthening is another effective strategy to further enhance the FCC+L1 2 structured Ni-based superalloy. In this work, we extend such approaches to high-entropy alloys (HEAs) by introducing different Al and Ti contents (5 at.% ∼18 at.%) into a Ni-based CrFe 2 Ni 4 alloy to form FCC+L2 1 heterostructured Al x CrFe 2 Ni 4 Ti y HEAs. Detailed microstructural analysis indicates that L1 2 Ni 3 (Al,Ti)-type nanoparticles form in a (Ni,Fe,Cr)-rich FCC matrix. The volume fraction of L2 1 AlNi 2 Ti-type phase can be varied by adjusting the Al/Ti ratio and concentrations of Al and Ti. Higher Al and Ti contents promote L2 1 phase formation and higher Al/Ti ratio (>1) prohibits the high Ti-containing compounds such as D0 24 η -Ni 3 Ti and C14 Laves Fe 2 Ti phases, which are hard but brittle. Corresponding Young's modulus , Poisson's ratio , hardness, and the bulk to shear modulus ratio (B/G) can be readily modified. Compressive tests demonstrate that Al 1.5 CrFe 2 Ni 4 Ti 1.0 alloy with half FCC and half L2 1 phases possesses the optimal strength-ductility combination (with compressive yield strength of ∼1564 MPa and fracture strain of ∼28 %). DFT calculations were performed to elucidate relevant mechanisms. Sliding wear tests were also performed, which demonstrate superior wear resistance of the HEAs at both room and elevated temperatures, compared with a commercial Ni-based superalloy, UHT-Nickel.
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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".