Application of Multiaxial Fatigue Analysis Methodologies for the Improvement of the Life Prediction of Landing Gear Fuse Pins
Bibliographic record
Abstract
Fuse pins are used in landing gear designs to attach the landing gear to the airframe and are designed to allow for a controlled separation of the landing gear from the aircraft structure in the event of a crash. Traditional uniaxial fatigue analysis methods have been found to be insufficient for properly predicting the fatigue life of the fuse pins; often significantly over-predicting or under-predicting the fatigue life. To improve the life prediction of these pins, multiaxial fatigue analysis methods were selected and implemented into a custom fatigue analysis program. The analysis procedure includes the constitutive modeling of the elastic-plastic material, the notch correction methods, cycle counting method and the fatigue damage criteria. The results of predictions made using the multiaxial fatigue methods for three fuse pin designs were compared to data from fatigue tests of three different landing gear assemblies. It was found that the performance of the constitutive model used for predicting the elastic and plastic stresses and strains, and the choice of fatigue damage criterion had the most effect on the final predicted fatigue life.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".