Comparison of Four Friction Models: Feature Prediction
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we provide not only key knowledge for friction model selection among candidate models but also experimental friction features compared with numerical predictions reproduced by the candidate models. A motor-driven one-dimensional sliding block has been designed and fabricated in our lab to carry out a wide range of control tasks for the friction feature demonstrations and the parameter identifications of the candidate models. Besides the well-known static features such as break-away force and viscous friction, our setup experimentally demonstrates subtle dynamic features that characterize the physical behavior. The candidate models coupled with correct parameters experimentally obtained from our setup are taken to simulate the features of interest. The first part of this work briefly introduces the candidate friction models, the friction features of interest, and our experimental approach. The second part of this work is dedicated to the comparisons between the experimental features and the numerical model predictions. The discrepancies between the experimental features and the numerical model predictions help researchers to judge the accuracy of the models. The relation between the candidate model structures and their numerical friction feature predictions is investigated and discussed. A table that summarizes how to select the most optimal friction model among a variety of engineering applications is presented at the end of this paper. Such comprehensive comparisons have not been reported in previous literature.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it