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Record W4302759761 · doi:10.1088/1361-665x/ac980e

3D printed multi-material polylactic acid (PLA) origami-inspired structures for quasi-static and impact applications

2022· article· en· W4302759761 on OpenAlexafffund
Anastasia L. Wickeler, Hani E. Naguib

Bibliographic record

VenueSmart Materials and Structures · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversity of TorontoUniversity of New Brunswick
FundersNatural Resources Canada
KeywordsPolylactic acidFinite element methodStructural engineeringCompression (physics)Square (algebra)Drop test3d printedMaterials scienceComposite materialMechanical engineeringEngineeringGeometryMathematicsPolymerManufacturing engineering

Abstract

fetched live from OpenAlex

Abstract Origami patterns can be used to inspire the designs of structural materials with beneficial properties, such as low strength-to-weight ratios. This study explores the design, manufacturing, and mechanical properties of three different origami-inspired shapes, as well as three different material combinations for each shape, through dynamic impact testing and quasi-static compression testing. The commonly studied Miura origami pattern will be compared to two uncommon patterns: a square-based pattern and a triangular-based pattern. The samples are 3D printed and the material combinations include one rigid and one flexible polylactic acid (PLA) sample, and one multi-material configuration with flexible PLA crease areas and rigid PLA origami faces. The rigid square sample was the most effective at absorbing a single drop-weight impact load and the flexible Miura pattern was most effective at absorbing impact loads when multiple drops were performed on the same sample. The rigid triangular structure withstood the highest loads during the quasi-static compression testing. A finite element model of the quasi-static compression test was built to enhance the analysis of the various tested configurations.

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 categoriesMeta-epidemiology (narrow)
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 score0.069
Threshold uncertainty score1.000

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 head0.012
GPT teacher head0.259
Teacher spread0.248 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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