Rubber Fatigue Revisited: A State-of-the-Art Review Expanding on Prior Works by Tee, Mars and Fatemi
Bibliographic record
Abstract
Rubber materials can endure substantial deformation while avoiding permanent damage or rupture, making them highly suitable for applications in the automotive industry and other sectors, particularly for noise and vibration reduction. However, rubber experiences degradation over time as defects or cracks appear and propagate under fluctuating loads. Therefore, it is of critical importance to prevent the failure of rubber components during service. As highlighted in prior literature surveys by Tee et al. in 2018, Mars and Fatemi in 2002 and 2004, significant research has focused on the mechanics and analysis of rubber fatigue. This body of work has grown rapidly and continues to evolve. Therefore, this study aims to compile and analyze the vast body of recent research on rubber fatigue conducted over the last decade, supplementing the reviews by Tee et al. in 2018, Mars and Fatemi in 2002 and 2004. The gathered studies were analyzed to identify current trends and emerging research gaps in the fatigue study of rubber, including advanced composite rubber materials such as magnetorheological elastomers (MREs). This review emphasizes the analysis techniques and fatigue experiments available for fatigue life prediction in rubber materials, while illustrating their practical applications in engineering analyses through specific examples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".