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Record W7116085775 · doi:10.82417/nemn-xt26

Development of a snow sports helmet integrating a 3D-printed triply periodic minimal surface foam

2025· other· en· W7116085775 on OpenAlexaff

Bibliographic record

VenueEspace ÉTS (ETS) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsSnowDissipationDrop impactsports equipmentSnow removalDrop (telecommunication)

Abstract

fetched live from OpenAlex

Helmets are a critical component of personal protective equipment for sports and recreational activities that involve a risk of head impacts. Their primary function is to mitigate severe traumatic brain injuries (TBI), especially in snow sport activities. In these disciplines, helmets are generally made with expanded polystyrene (EPS), a material engineered to undergo plastic deformation upon impact. This energy dissipation mechanism protects well against severe TBI but is less effective against mild TBI, including sport-related concussions (SRC). Snow sports are widely practiced by young individuals whose neurodevelopmental processes are still ongoing, rendering them more vulnerable to SRC. Even a seemingly minor fall on compacted snow can result in SRC, underscoring the need for helmet designs that extend protection beyond severe trauma to include lower-impact brain injuries. Given this understanding, there is a compelling rationale for developing snow sports helmets optimized for mitigating SRC.This study seeks to address this limitation by designing and assessing the performance of a snow sports helmet prototype integrating a 3D-printed triply periodic minimal surface (TPMS) foam. The mechanical behavior of twelve TPMS foam samples with varying unit cell topology and density was characterized under impact loading using a drop tower. The stress-strain (SS) curves of each TPMS configuration were then compared to the SS curve of a conventional EPS sample to identify the most promising TMPS structure based on its energy absorption properties. The EPS liner of a reference snow sports helmet (Decathlon, WEDZE H100) was replaced with a 3D-printed liner featuring the selected TPMS, specifically a gyroid unit cell structure with a 6 mm characteristic dimension and a relative density of 40%, resulting in a helmet prototype. The safety performance of both the reference and prototype helmets was subsequently evaluated using a protocol inspired by the Snow Sports Helmet STAR Protocol (Virginia Tech Helmet Lab). This protocol involves impact testing on a drop tower under six distinct configurations, defined by a single impact speed (6.7 m/s), three impact locations (front, side and rear boss) and two flat-angle anvil (35° and 55°). A STAR value, calculated from the peak linear acceleration and rotational velocity, was determined for each helmet, with a lower score indicating improved safety performance. The prototype helmet (0.65) exhibited a STAR value comparable to the value of the reference helmet (0.66). Accordingly, future work will focus on optimizing the TPMS structure by refining cell size and relative density to enhance protective performance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.143
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.012
GPT teacher head0.261
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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