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3D printed bistable composite lattice shells with tailorable coiled geometries

2025· article· en· W4411196479 on OpenAlexafffund
Nicholas Elderfield, Aghna Mukherjee, Paolo Ermanni, J. Wong

Bibliographic record

VenueComposite Structures · 2025
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis and Optimization
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsBistabilityComposite numberMaterials scienceLattice (music)3d printedComposite material3D printingStructural engineeringOptoelectronicsEngineeringPhysicsBiomedical engineeringAcoustics

Abstract

fetched live from OpenAlex

This investigation presents, to the authors’ best knowledge, the first 3D printed continuous fiber-reinforced polymer composite deployable booms. The precise material placement capabilities of the fused filament fabrication (FFF) process are leveraged to produce cylindrical bistable slit tube booms with lattice architectures. The influences of fiber angles, lattice density, and initial shell curvature on the existence and form of stable coiled configurations as well as flexural rigidity properties are investigated. A computational procedure for automatically generating finite element models directly from material deposition paths is presented, with predicted shell behaviors showing strong agreement with experimental results using both homogenization and full-scale modeling approaches. Lattice shell architectures are revealed to exhibit higher flexural rigidity properties than continuum architectures on an equal-mass basis. Finally, bistable slit tube booms that can coil into unique stable configurations via the tailoring of material deposition paths are demonstrated.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.202
Teacher spread0.197 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2025
Admission routes2
Has abstractyes

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