Improving the Abrasion Resistance of “Green” Tyre Compounds
Bibliographic record
Abstract
Since the introduction of "Green Tyres" in the early 90's, the use of silica as a reinforcing filler, along with a silane coupling agent, has spread and grown worldwide. The greatest advantage of using silica over carbon black as a reinforcing filler in a tyre tread compound is that a lower rolling resistance is achieved, while maintaining good wet traction. However, a previous study has shown that the wear resistance of a silica filled epoxidised natural rubber (ENR) compound was not as high as those of conventional Oil Extended Styrene Butadiene rubber (OESBR) and NR/BR compounds used in passenger car and truck tyre treads. In this work, with the aim of improving abrasion resistance, the effect of blending Butadiene rubber (BR) into a silica filled ENR compound was studied. Blends with 0 to 30 phr BR were prepared in a Polylab Haake internal mixer. The rheological properties of the compounds were measured using a Mooney viscometer and Oscilating Disc Rheometer. The hardness, tensile strength and DIN abrasion resistance were also measured. The results showed that the ENR/silica compound properties such as tensile strength and hardness were as good as those of the conventional compounds. However, the most important finding was that abrasion resistance increased significantly with BR content, exceeding that of the conventional compound at BR:ENR ratios of greater than 20:80.
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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".