Prediction of the static pressure induced by a foam earplug inside a cylindrical earcanal
Bibliographic record
Abstract
Over 120 million workers across the globe are exposed to dangerous levels of noise. Daily, 360,000 workers (Quebec province) are exposed to noise levels (90 dBA) that could cause hearing loss. One way to protect workers is the usage of hearing protection devices (HPDs). The use of HPDs is not efficient for wearers because they are often worn incorrectly or inconsistently. The most significant cause is discomfort induced by HPDs. This thesis is part of large project and focused on the prediction of the static mechanical pressure (SMP) on human’s earcanal wall induced by foam earplug, which is one of the sources of discomfort mentioned in the literatures. Currently, there is no available test bench or methods to measure the SMP at the interface between foam earplug and human earcanal. Accordingly, the main objective of this master thesis is to predict the SMP exerted by a foam earplug inserted into a simplified cylindrical earcanal. The specific objectives aimed at building finite element models (FEM) with two levels of complexity: 1) a model that simulates the insertion of the foam earplug in a simplified rigid earcanal of cylindrical shape without skin layer; 2) a model that simulates the insertion of the foam earplug in a more realistic earcanal that includes the surrounding soft tissue (skin layers). An earplug (3M classic E.A.R made of PVC foam) is considered. The mechanical properties of the human skin and foam earplug are characterized. The characterized mechanical properties of human skin and foam earplug were validated by using numerical simulation (FEM). A very good correlation is obtained from results of transverse and axial compression tests of foam earplug in both experimental and numerical simulation. The numerical simulation results for the force-displacement relationship obtained in indentation test on skin show a very good match with experimental data. The contact force between foam earplug and rigid cylindrical earcanal was measured by experimental test. The numerical simulations are carried out to mimic experimental tests. The contact forces were computed at the interface between the foam earplug and the rigid cylindrical earcanal was approximately 1.6 N without a skin layer and 1.5 N with a skin layer. The SMP at the interface between the foam earplug and the rigid cylindrical earcanal were 3.40 kPa without a skin layer and 3.18 kPa with a skin layer.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".