Stiffness and damping tuning through using a piezoelectric friction damper and a layered structure
Bibliographic record
Abstract
Abstract Vibration suppression is essential for enhancing the performance of mechanical systems, as it prevents structural damage and minimizes noise. Various methods, including passive, semi-active, and active approaches, have been developed to achieve this goal. Among these, friction dampers, primarily categorized as passive, are highly efficient in adjusting system damping and influencing energy dissipation. By modulating the normal force in the friction damper based on external force intensity, performance can be further enhanced. This study employs a piezoelectric actuator to regulate the normal force and introduces an analytical method along with finite element modeling to estimate the normal force in the friction damper. A layered structure is introduced as an additional mean to tune damping and stiffness. The performance of the semi-active piezoelectric friction damper is investigated in free and forced vibrations, including flexural and axial cyclic loads. Furthermore, the advantages of employing layered structures are investigated experimentally. Overall, the piezoelectric friction damper demonstrates effective energy dissipation during macroslip events. Nevertheless, in case of microslip, increasing the actuator voltage results in reduced damping and a marginal rise in stiffness.
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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 | 0.000 |
| 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) | 0.001 | 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".