A comparison of friction modifier performance using two laboratory test scales
Bibliographic record
Abstract
This paper describes two methods, carried out at two different test scales, for assessing the friction modifier performance. Study A used the wear data from a full-scale rig test at the voestalpine Schienen GmbH and compared it with the wear data from twin disc tests using the SUROS test machine at the University of Sheffield. Study B compared the ‘retentivity’ data, from a full-scale rig at the University of Sheffield, with the data from the SUROS tests. Study A concluded that a good correlation existed between the two scales although assumptions made in the full-scale contact calculation introduce a large spread into the results. There was a greater correlation between the two data sets at more severe contact conditions. Study B showed a different baseline coefficient of traction between the two scales and that a longer test length is required to fully evaluate the ‘retention’ of the friction modifier on the full-scale rig. The paper expands on a previous conference presentation on the same subject. Additional information on the test procedure and test rigs is included here. Surface and subsurface analyses of the SUROS test samples have also been added. The analyses have shown that applying the friction modifier leads to a similar wear mechanism as for the dry contact, but the wear is less severe and there is less subsurface deformation. A discussion describing the differences in test scales and comparing lab tests to field operation is also included.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".