Investigation of Mechanical Properties and Wear Resistance of A2/B2 Type Medium-Entropy Alloy Matrix Reinforced with Tungsten Particles by In-Situ Reaction
Bibliographic record
Abstract
Microstructure, mechanical properties, wear resistance, corrosion and corrosive wear resistance of AlCrFeNiWx (x = 0, 0.1, 0.2, 0.3 and 0.4) medium-entropy alloys (MEAs) prepared by vacuum arc melting process were investigated. Results of the study show that the microstructure of as-cast AlCrFeNiWx alloys is composed of one disordered Fe-Cr rich BCC phase (A2) and one ordered NiAl-rich (B2) phase when x = 0 and 0.1; W particles appear when x ≥ 0.2. Hardness and strength of AlCrFeNi alloy are markedly increased by the W addition. The strengthening mechanisms include solid-solution strengthening, nano-sized precipitation strengthening and second phase strengthening. The excellent ductility of AlCrFeNi is retained with minor W addition (x ≤ 0.2) but it considerably declines as more W is added, resulting from the precipitation of excessive large-sized W particles. W addition improves the pitting resistance and passivation property of AlCrFeNi HEA in 3.5 wt. % NaCl solution. It is shown that AlCrFeNiW0.3 possesses the highest corrosion resistance, as reflected by the highest Ecorr and the lowest Icorr. Tungsten notably enhances the resistance of this HEA to wear and corrosive wear. AlCrFeNiW0.4 with the highest strength and hardness exhibits the lowest wear volume loss under both dry and corrosive wear conditions.
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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".