Effect of Surface Morphology and Texture of Short-Tailed Shrew’s Toe on Tribological Properties of 65Mn Steel
Bibliographic record
Abstract
To reduce the friction coefficient and wear in tillage machinery during operation, biomimetic textures with different densities inspired by the short-tailed shrew's claw were designed using biomimetic principles. These textures were applied to the surface of 65Mn steel using laser processing technology. This study investigated the effects of these bionic textures on the tribological properties of 65Mn steel surfaces in two environments: dry friction and soil friction. Friction and wear tests were conducted, and the friction coefficient, wear morphology, and wear quality were measured using a friction and wear testing machine, a scanning electron microscope (SEM), and a three-dimensional profilometer. The results indicate that under dry friction conditions, the tribological properties of specimens with bionic textures were significantly improved compared to non-textured specimens. The frictional properties of the specimens with bionic textures were optimized at a texture density of 20%, with an average coefficient of friction reduction of 24%. Under soil friction conditions, the samples with bionic textures demonstrated better tribological performance at densities of 20% and 30% compared to the non-textured samples, with decreases in the average coefficient of friction of 1.3% and 2.9%. The special surface structure of the bionic short-tailed shrew claw can effectively reduce friction heat effects and wear, demonstrating significant anti-friction and anti-wear performance.
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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) | 0.000 | 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".