Enlarging the Temperature Range for Maximum Wear Resistance of TiNi Alloy Using a NTE Phase
Bibliographic record
Abstract
The near-equiatomic TiNi alloy has been demonstrated to possess high wear resistance, which largely benefits from its pseudoelasticity (PE). However, the PE occurs only in a small temperature range, which makes the wear resistance of this alloy unstable as temperature changes, caused by environmental instability or frictional heating. Therefore, enlarging the working temperature of PE could considerably improve this alloy as a novel wear-resistant material. One possible approach is to develop a self-built temperature-dependent internal stress field by taking the advance of the difference in thermal expansion between the pseudoelastic matrix and a reinforcing phase. Such a T-dependent internal stress could adjust the martensitic transformation temperature to respond changes in environmental temperature so that the temperature range of PE could be enlarged, thus leading to a wide temperature range in which the minimum wear loss is retained. Research was conducted to investigate effects of an added second phase having a negative thermal expansion (NTE) coefficient on the wear resistance of a near-equiatomic TiNi alloy. It was demonstrated that the temperature range of this modified material in which the wear loss dropped was enlarged. In addition, the wear resistance of such a TiNi-matrix composite was on one order of magnitude higher than that of unmodified TiNi alloy.
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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".