Identification of Friction Parameters for Limited Relative Displacement Contacts
Bibliographic record
Abstract
Damping using dry friction has long been recognized as an effective control method for many vibration problems. However, given the strong nonlinear nature of friction, the theoretical and experimental investigations of associated non-linear control methods are much more difficult than for linear control methods. Moreover, the difficulty of identifying friction models parameters for Limited Relative Displacement (LRD) contacts is still a subject of research. This study first proposes an identification procedure to evaluate the ability of the LuGre friction model to predict small amplitude (30μm–150μm) frictionally damped vibrations for a LRD contact. An experimental setup implementing an ideal frictionally damped Single Degree Of Freedom (SDOF) oscillator connected to an electrodynamic shaker is then presented to study friction damping. The simulation results are assessed against the experimental results, demonstrating that the identification procedure is well suited to estimate the parameters of the LuGre friction model and that the model captures very well the friction phenomenon for small amplitude vibrations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".