Development of a Numerical Chain to Optimize Railway Axles with Respect to Fatigue Damage
Bibliographic record
Abstract
In today's competitive business environment, it has become increasingly important to reduce manufacturing and raw materials cost. For this purpose, an innovative process of design and manufacturing railway axles is developed. It is based on forging hollow axles which allows a significant reduction in steel consumption. In this work, we tried to analyze how these modifications induced by this new process and design impact the service behavior and particularly the durability face to cyclic loadings that can lead to fatigue failure. In the present study, a numerical chain has been developed going from the simulation of the manufacturing process up to the analysis in fatigue. In the first step, the forging process is modeled in order to predict the residual stress field and the initial plastic strain. From this initial condition, the assembly operation of the wheel on the axle is simulated before the redistribution of stresses and strains under cyclic load. The final objective is to obtain the cyclic loadingpaths, in order to provide the data needed for the analysis of fatigue.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".