Transformation from D022 to L12 in Al3Ti by Fe Addition for Enhanced Wear Resistance
Bibliographic record
Abstract
The addition of third elements may help transform brittle D022-structured lightweight Al3Ti to a relatively ductile L12-structured (Al, M)3Ti (where M represents the third elements), thus increasing the ductility at the expense of hardness. Such a transformation could benefit the wear resistance of the alloy due to improved toughness if a proper balance between the hardness and ductility is achieved. In this work, a D022-predominant Al3Ti alloy (S-Al3Ti) and an L12-predominant (Al, Fe)3Ti alloy (S-Al67Ti25Fe8) were fabricated by arc melting. Change in wear resistance, corresponding to a D022-to-L12 transformation, caused by the addition of Fe as a representative third element, was investigated and compared with the wear resistance of a commercial Al-matrix composite reinforced by 30 wt.% SiC particles (S-Al/SiCp) as a reference material. It was observed that wear of the S-Al3Ti resulted from abrasion involving synergistic oxidation, leading to a larger volume loss. In contrast, the softer S-Al67Ti25Fe8 showed enhanced wear resistance, benefiting from improved toughness with reasonable hardness. During the wear testing, both the alloys exhibited better performance than S-Al/SiCp, a well-known lightweight composite. This study highlights that D022-to-L12 transformation enhances wear resistance due to increased toughness which can be adjusted using the addition of a third element.
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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".