Identification of Friction Model Parameters Using the Inverse Harmonic Method
Bibliographic record
Abstract
A hybrid friction model has been developed by Azizian and Mureithi (2013, “A Hybrid Friction Model for Dynamic Modeling of Stick–Slip Behavior,” ASME Paper No. PVP2013-97249) to simulate the general friction behavior between surfaces in contact. However, identification of the model parameters remains an unresolved problem. To identify the parameters of the friction model, the following quantities are required: contact forces (normal and tangential or friction forces), the slip velocity, and the displacement in the contact region. Simultaneous direct measurement of these quantities is difficult. In the present work, a beam clamped at one end and simply supported with the consideration of friction at the other is used as a mechanical amplifier of the friction effects at the microscopic level. Using this simplified approach, the contact forces, the sliding velocity, and the displacement can be indirectly obtained by measuring the beam vibration response. The inverse harmonic balance method is a new method based on nonlinear modal analysis which is developed in this work to calculate the contact forces. The method is based on the modal superposition principle and Fourier series expansion. Two formulations are possible: a harmonic form formulation and a subharmonic form formulation. The approach based on subharmonic forms coupled with spline fitting gave the best results for signal reconstruction. Signal reconstruction made it possible to accurately identify the parameters of the hybrid friction model with a multiple step approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".