FACTORS AFFECTING THE FATIGUE LIFE AND FRACTURE AREA MACROMORPHOLOGY OF NATURAL RUBBER
Bibliographic record
Abstract
ABSTRACT The service life of rubber products lacks a comprehensive evaluation of the factors that influence rubber fatigue life and an accurate prediction of their fracture behavior. To address this gap, we developed a protocol to investigate the working conditions that affect the fatigue behavior of rubber and document changes in the fracture area during its fatigue life. We subjected natural rubber specimens to various fatigue tests under changing conditions, including temperature, deformation, aging, sample geometry and frequency. For each experimental setup, we conducted a detailed examination of the fracture area, recording the rate and direction of crack propagation. Our observations revealed that the applied load within a dynamic deformation range substantially impacted fatigue life, providing valuable insights into the effects of these factors on material behavior. Furthermore, our macromorphological analyses unveiled distinct characteristics of crack propagation rates and directions associated with each factor at different stages of the fatigue life of the rubber samples. Additionally, the surface temperature elevated as deformation levels increased, highlighting the need to consider this factor in conjunction with geometric, mechanical, and environmental influences on rubber fatigue life. The combined effect of these parameters has the potential to either extend or shorten the fatigue life of natural rubber.
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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) | 0.000 | 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".