Non-Uniform Pressure Distribution in Draw-Bend Friction Test and a New Methodology to Determine Non-Constant Friction Coefficient From the Test
Bibliographic record
Abstract
Friction under lubricated conditions is known to depend on pressure, and experimental determination of this dependence has typically quantified pressure as an average value over the contact area. However, non-uniform pressure distributions at the contact interfaces of draw-bend tests have been reported from various experiments and simulations. A previous study by the authors has evaluated the current methodology, which assumes uniform pressure distribution to estimate friction coefficients from draw-bend friction tests, and has concluded that the current methodology is only valid for measuring an average friction coefficient over the pressure range, which exists in the specific draw-bend system. In this paper, a new methodology to extract non-constant friction coefficients from draw-bend friction tests is suggested. In the methodology, contact pressure maps obtained from simulations, instead of the uniform pressure assumption, are included in the analysis of test data to measure the pressure dependency of friction coefficient. The methodology is tested by applying the method to back predict the input friction data from finite element simulation results of draw-bend friction tests, in which non-constant friction models are used as friction input.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".