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Record W4415927353 · doi:10.15353/hi-am.v1i1.6808

You are what you breathe: observing airborne carbon fiber particulates during FFF printing of PA6-CF filament

2025· article· W4415927353 on OpenAlexaff
Dora Strelkova

Bibliographic record

VenueProceedings of the Holistic Innovation in Additive Manufacturing (HI-AM) Conference · 2025
Typearticle
Language
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsFused filament fabricationFiberProtein filamentSlicingFabricationDispersion (optics)Filter (signal processing)

Abstract

fetched live from OpenAlex

Carbon fiber reinforced filaments are increasingly popular in Additive Manufacturing (AM) due to their enhanced mechanical properties compared to traditional materials like PETG, ABS, and Nylon. However, these materials present challenges, including proper drying requirements and potential fiber transfer to the skin during handling. In this study, microscopic examination of fingertips after handling PA6-CF parts revealed significant fiber transfer, raising concerns about airborne fiber dispersal during printing. This research aims to observe the dispersion of fibers from PA6-CF filament using a Bambu Lab X1C desktop Fused Filament Fabrication (FFF) system. A custom apparatus with filter was developed to capture dispersed fibers during printing experiments. The used filters were then observed for fiber content to draw conclusions. Safety precautions are recommended along with a proposed alternative slicing method to minimize fiber transfer from final parts. This study contributes to a safer working environment for 3D printing enthusiasts and professionals by addressing potential health risks associated with airborne fiber dispersion.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.002
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 head0.027
GPT teacher head0.246
Teacher spread0.219 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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