Custom molded silicon earplugs: effect of material properties on acoustic attenuation and mechanical skin contact
Bibliographic record
Abstract
Silicone is one of the most common used material for custom earplug manufacturing. Nevertheless, the influence of its mechanical properties on both the sound attenuation and the mechanical skin contact are based on empirical models. The knowledge of the silicon properties is frequently limited to its shore value which is necessary but not sufficient to fully describe the vibro-acoustic behavior of the earplug. In this work, various sensitivity analyses based on 2D-axisymmetric finite element model of the occluded auditory canal coupled to mechanical properties measurements of silicon commonly used for custom earplugs are performed. Complementary attenuation measurements with acoustical tests fixtures are made in a means of model validation. The achievable values around realistic material properties variations are presented from the two perspectives of sound attenuation and mechanical impedance modulus at the contact between the earplug and the auditory canal. The rigidity or mass-frequency law effects are retrieved and mostly quantified. Unexpected or contradictory effects are also highlighted, for example a sign change around the mean attenuation value at two consecutive frequencies or an unexpected coupling between two parameters. These results contribute to filling the knowledge gap in terms of complex mechanical behavior of silicone earplugs and could benefit to earplug manufacturers for product optimization.
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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) | 0.000 | 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".