LOAD SEQUENCE ANALYSIS IN FATIGUE LIFE PREDICTION
Bibliographic record
Abstract
In this work, the load sequence effect is analyzed in fatigue test. One of the assumptions of the Miner’s rule is that the total damage is equal to the sum of the damages absorbed; however, different models have been proposed to take the effect of the load sequences under two load levels into account. To analyze this effect, a case study of a rear axle mounting bracket has been performed, analyzing six different sequences of three load levels, defined as Low, Medium and High. A Finite Element Analysis was also performed using MSC Tools. With these results and a series of test at constant amplitude, the component S-N curve was made. 24 tests at room temperature were performed in order to evaluate the damage process. It was found that, under a block of three load levels, the sequence of each block has an effect in the total amount of damage under the same number of cycles. With this information it is possible to improve the life prediction through the modification of the damage rule. The proposed model uses a factor which depends on the ultimate strength and yield point. This is an advantage over other approaches, as the other models need additional dynamic tests to obtain coefficients to perform the life prediction.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".