Validation of Friction Model Parameters Identified Using the Inverse Harmonic Balance Method
Bibliographic record
Abstract
A hybrid friction model was recently developed by Azizian and Mureithi [1] to simulate the friction behavior of tube-support interaction. However, identification of the model parameters remains unresolved. In previous work, the friction model parameters were identified using reverse the harmonic method, where the following quantities were indirectly obtained by measuring the vibration response of a beam: friction force, sliding speed of the force of impact and local displacement at the contact point. In the present work, the simulation by the finite element method (FEM) of a beam clamped at one end and simply supported with the consideration of friction effect at the other is conducted. This beam is used to validate the inverse harmonic balance method and the parameters of the friction models identified previously. Two static friction models (the Coulomb model and Stribeck model) are tested. The two models produce friction forces of the correct order of magnitude compared to the friction force calculated using the inverse harmonic balance method. However, the models cannot accurately reproduce the beam response; the Stribeck friction model is shown to give the response closer to experiments. The results demonstrate some of the challenges associated with accurate friction model parameter identification using the inverse harmonic balance method. The present work is an intermediate step toward identification of the hybrid friction model parameters and, longer term, improved analysis of tube-support dynamic behavior under the influence of friction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".