The effect of hardness matching of rail/wheel materials on wear rate of railway wheel
Bibliographic record
Abstract
There is no rationalization for a certainty that harder wheels or rails will result in an increase in wear of the opposite side of the wheel/rail interface. This research investigated how the wear of wheel material changes when the hardness of the opposing pair is varied. Three Rail/wheel material matches; normalized UIC50 kg/m and S1002 wheel profile (Rail/wheel material 1), normalized UIC60 kg/m and whole heat treated S1002 wheel profile (Rail/wheel material 2) and rim heat treated UIC60 kg/m and whole heat treated S1002 wheel profile (Rail/wheel material 3) has been investigated using multi-body simulation software (SIMPACK) and MATLAB programming. For validation, as an experimental advantage, the wear depths measured on the wheel tread wear of the end vehicle of LRT for mileage of 50,000 km are compared to the results of numerical simulation performed. As a result, the estimated total tread wear amount after a mileage of 50,000 km is 4% larger than the experimental one. That is indeed a very good result considering that either component of the wheel wear prediction model used is neither adjustment nor calibration. From the three rail/wheel matches, Rail/wheel material 3 found to be the better material match that could resist wear significantly considering material hardness as important criteria for comparison. The study could remark that, despite the fact both wheel and rail material hardness could affect the wear performance in respected positive ways, most significant improvements are attained by improving the rail material hardness ahead of wheel material.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".