Effect of Surface Nanocrystallization on Wear Behavior of Steels: A Review
Bibliographic record
Abstract
Ferrous alloys, particularly steels, form a specialized class of metallic materials extensively employed in industrial sectors to combat deterioration and failures caused by wear. Despite their commendable mechanical properties, steels are not immune to wear-induced degradation. In this context, surface nanocrystallization (SNC) technologies have carved a distinct niche for themselves by enabling the nanostructuring of the surface layer (with grain sizes < 100 nm). This process enhances overall mechanical properties to a level desirable for wear resistance while preserving the chemical composition. Existing literature has consistently highlighted the efficacy of various SNC methods in improving the wear resistance of ferrous alloys, positioning SNC as a promising tool to extend materials' service life in practical applications. This review provides a comprehensive examination of the SNC techniques employed in surface treatment of ferrous alloys and their impact on wear behavior. We delved into the underlying mechanisms governing wear in SNC-treated Fe-based alloys and concluded with a discussion on current challenges and future perspectives in this evolving field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".