Dynamic stiffness identification methods: experimental study and validation
Bibliographic record
Abstract
In a vehicle, the operation of mechanical systems generates undesirable vibrations and noise, and their reduction requires the study of three parts: vibrating systems, receiving structures, and connecting interfaces. The study presented in this paper concerns the connecting interfaces between subsystems. In particular, the in situ characterization of the isolation interface between two substructures by measuring dynamic transfer stiffness. In contrast to current methods, which require the disassembly of the vibration isolator from its original assembly, in situ methods have been studied and validated. This study sheds light on the strengths and limitations of each approach. These are known as the direct in situ method (D-IS) and round trip in situ method (RT-IS). Experimental validation has been carried out by comparing the D-IS method to the resonance method on a simple system. Good agreement is obtained between the two methods, making it possible to validate the method at low frequencies. Then, a comparison between the two methods D-IS and RT-IS is carried out on a more resonant test bench. The obtained stiffnesses were further validated using transfer path analysis (TPA) techniques to verify the performance of the proposed methods, and it was used to predict the response of another system, showing thus the independent nature of the identified dynamic 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".