Recent Progress on Wear‐Resistant Materials: Designs, Properties, and Applications
Bibliographic record
Abstract
There has been tremendous interest in the development of different innovative wear-resistant materials, which can help to reduce energy losses resulted from friction and wear by ≈40% over the next 10-15 years. This paper provides a comprehensive review of the recent progress on designs, properties, and applications of wear-resistant materials, starting with an introduction of various advanced technologies for the fabrication of wear-resistant materials and anti-wear structures with their wear mechanisms. Typical strategies of surface engineering and matrix strengthening for the development of wear-resistant materials are then analyzed, focusing on the development of coatings, surface texturing, surface hardening, architecture, and the exploration of matrix compositions, microstructures, and reinforcements. Afterward, the relationship between the wear resistance of a material and its intrinsic properties including hardness, stiffness, strength, and cyclic plasticity is discussed with underlying mechanisms, such as the lattice distortion effect, bonding strength effect, grain size effect, precipitation effect, grain boundary effect, dislocation or twinning effect. A wide range of fundamental applications, specifically in aerospace components, automobile parts, wind turbines, micro-/nano-electromechanical systems, atomic force microscopes, and biomedical devices are highlighted. This review is concluded with prospects on challenges and future directions in this critical 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".