Microscopic dynamic observation of adhesion hysteresis friction and exploration of the influence of different pressures on friction transmission
Bibliographic record
Abstract
Abstract The mechanism of adhesive friction between viscoelastic materials is a key question. In this study, the friction process of the adhesive interface between a friction lining and a wire rope is dynamically observed in real time to analyze the adhesion hysteresis friction intuitively and quantitatively. The adhesion is determined by the state of motion, while the relative displacement of the wire rope and lining is used to find the magnitude of the adhesive friction. The hysteresis friction is reflected by the internal deformation of the lining. The magnitude of the hysteresis friction is determined by the displacement difference (Δx) in the sliding direction of two marked points at different distances from the contact surface. The results show that the adhesion friction is proportional to the loss modulus and the hysteresis friction is proportional to the ratio of the loss modulus to the square of the storage modulus (E″/(E′2)). The frictional vibration first decreases and then increases with the increase in pressure. The K25 lining has the highest adhesion hysteresis friction and minimal frictional vibration. The result provides a simple and intuitive method for research into the friction transmission and vibration of viscoelastic materials.
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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".