LPBF-Produced Elastomeric Lattice Structures for Personal Protection Equipment: Mechanical Performance Versus Comfort-Related Attributes
Bibliographic record
Abstract
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.
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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 | 0.000 | 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".