Multiaxial Fatigue-Life Prediction of Railway Axles with Consideration of Braking Effects
Bibliographic record
Abstract
Ensuring safety in the design of railway infrastructure and rolling stock is a critical aspect of railway operations.Railway axles are subjected to fluctuating bending and torsion loads, which often lead to the development of plastic stresses in the vicinity of fillets and notches, even though they are designed to withstand nominal elastic loads.This study aims to provide an accurate fatigue-life prediction for a motor bogie axle, taking into account the braking effects in conjunction with press fitting and the influence of masses in motion on the axle.Initially, the stress and deflection of the axle under combined loadings are calculated in accordance with the EN 13104 standard.Subsequently, a comparative simulation analysis is conducted using ABAQUS commercial software, with particular emphasis on specific loading conditions.A strainbased approach is employed to estimate fatigue life under multiaxial loading and variable amplitude overloading scenarios, utilizing fe-safe commercial fatigue analysis software.The results reveal a strong correlation between the theoretical and finite element analyses.The braking effects are found to reduce fatigue life by 50% compared to the influences of press fitting combined with masses in motion.This research highlights the significance of considering braking effects in fatigue-life predictions of railway axles to ensure safe and robust design.
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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 | 0.000 | 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".