Adjustable mold for the manufacture of reusable, personalized N95masks
Bibliographic record
Abstract
During the COVID-19 pandemic, healthcare workers faced several challenges while adapting to the regular use of N95 face masks: skin damage after extended use, headaches due to re-inhaled CO2, imperfect airtight seal with the user's face, and excessive waste caused by disposable masks. Here we discuss a mask design and manufacturing process for mitigating these concerns, in which an adjustable mold is used to rapidly produce reusable, custom-fitted masks. Topography analysis of human head forms and anthropometric survey data were used to create a parametric mask design based on eight control points which can be adapted to fit 99.7% of the population. Measurements are taken from a 3D-scanned model of the user's head and used to adjust the dimensions of a flexible silicone mold, in which key parametric points are driven by linear actuators. Rotational molding is then used to produce silicone masks from liquid silicone resin: a process requiring very low initial investment compared to injection molding, and which can scale to produce many masks simultaneously. This work reports on the development of the parametric model and mask design, the novel rotational molding system for manufacturing personalized masks, and the development of three personalized masks. Results from initial materials and fit testing are presented. The results indicate that the proposed design and manufacturing process has the potential to develop more effective and sustainable PPE. Future work includes increasing the precision of the prototype manufacturing system and additional materials testing to improve mask performance.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".