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Record W4410858623 · doi:10.3390/jmmp9060182

LPBF-Produced Elastomeric Lattice Structures for Personal Protection Equipment: Mechanical Performance Versus Comfort-Related Attributes

2025· article· en· W4410858623 on OpenAlexaff
William Turnier Trottier, Thierry Krick, Vladimir Braïlovski

Bibliographic record

VenueJournal of Manufacturing and Materials Processing · 2025
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsElastomerLattice (music)Materials scienceStructural engineeringComposite materialEngineeringPhysicsAcoustics

Abstract

fetched live from OpenAlex

This study focuses on the energy absorption and wearer comfort attributes of regular lattice structures fabricated by laser powder bed fusion from two elastomeric materials, namely TPU1301 and TPE300, for use in personal protective equipment (PPE). This study compares Body-Centered Cubic (BCC), Face-Centered Cubic (FCC) and Kelvin (KE) lattice structures with density varying from 0.15 to 0.25 g/cm3, cell size varying from 10 to 14 mm and feature size varying from 1 to 3 mm. Quasi-static and dynamic compression testing confirmed that among the studied geometries, KE structures printed with TPE300 powders provide the best combination of reduced peak acceleration and increased compliance, thereby improving both safety and comfort. Using the protection–comfort maps built on the basis of this study enables the design of lightweight and compact protective structures. For example, if a safety layer protecting a 100 mm2 surface area can be manufactured from either TPE300 or TPU1100 powders using either KE or FCC structures, the KE TPE300 layer will be 1.5 times thinner and 2.5 times lighter than its FCC TPU1301 equivalent. The results of this study thus provide a basis for the optimization of lattice structures in 3D-printed PPE to meet both service and manufacturing requirements.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.630

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.234
Teacher spread0.220 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations3
Published2025
Admission routes1
Has abstractyes

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