Influences of Structural and Friction Parameters on Transformation of Contact State in Friction-Induced Vibration
Bibliographic record
Abstract
Abstract To investigate the effects of friction operation conditions and vibration system parameters on the friction states transformation during vibration, the friction-induced vibration (FIV) mathematical model of a single-degree-of-freedom (SDOF) system in the direction of friction is introduced. The vibration response is solved by an iterative method combining kinematics and vibrology. The velocity and direction changes of the friction force caused by the sliding of the moving plate on top of the mass will lead to the ‘rebound’ phenomenon and the transformation of contact states. Numerical simulation and parameter studies were used to investigate the effects of stiffness constant, mass, normal force, velocity, damping ratio, and friction coefficient on the conversion of friction state, vibration response, and friction history. The numerical results show that the time of mass block and plate staying on stick status increases with normal force, static friction coefficient, the delay rate of relative sliding velocity control coefficient, and decreases with the increment of stiffness, velocity, mass, the lower limit of static friction coefficient and damping ratio. In the absence of general quantitative definitions and conclusions, the trend of this paper is to show the change of friction state during FIV through an accurate physical system.
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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) | 0.000 | 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) | 0.001 | 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".