AN EXPERIMENTAL STUDY ON FATIGUE CHARACTERISTICS FOR VISCOELASTIC SUSPENSIONS OF CONSTRUCTION VEHICLES
Bibliographic record
Abstract
To acquire the fatigue characteristics of viscoelastic suspensions mounted in newly-developed construction vehicles, an experimental study of fatigue characteristics should be carried out on the vibration damping rubber suspension components. However, due to the lack of corresponding fatigue failure criteria for the damping rubber components, how to determine the parameters for the accelerated fatigue experiments becomes a new research subject. Based on the accelerated fatigue experiments on the viscoelastic suspensions, the parameters on fatigue failure characteristics such as the cycle index (ne), the slope (k) of S–N logarithm curve and the allowable temperature rise, and so on, were investigated. To develop a new viscoelastic suspension for the crawler bulldozer, three types of rubber made from different formulas for the viscoelastic suspensions were studied and their experimental loads were divided into 8 levels in terms of the practical working conditions of the bulldozer. The accelerated fatigue experiments of viscoelastic suspensions were performed and the obtained parameters were applied to the fatigue experiments for the suspensions of a newly-designed 386 kW crawler bulldozer. Under the industrial application over 4,000 hours, the results were shown to be consistent with the predicted experimental values.
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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 | 0.001 | 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".