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Record W2338186794 · doi:10.1117/12.2222093

Characterization of origami shape memory metamaterials (SMMM) made of bio-polymer blends

2016· article· en· W2338186794 on OpenAlexaff
Mohamed Ali E. Kshad, Hani E. Naguib

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversity of Toronto
FundersYükseköğretim KuruluCouncil for Higher EducationMinistry of Education, Libya
KeywordsMaterials scienceShape-memory polymerMetamaterialShape-memory alloyCharacterization (materials science)FabricationMolding (decorative)PolymerViscoelasticityNanotechnologyComposite materialOptoelectronics

Abstract

fetched live from OpenAlex

Shape memory materials (SMMs) are materials that can return to their virgin state and release mechanically induced strains by external stimuli. Shape memory polymers (SMPs) are a class of SMMs that show a high shape recoverability and which have attractive potential for structural applications. In this paper, we experimentally study the shape memory effect of origami based metamaterials. The main focus is on the Muira origami metamaterials. The fabrication technique used to produce origami structure is direct molding where all the geometrical features are molded from thermally virgin polymers without post folding of flat sheets. The study shows experimental investigations of shape memory metamaterials (SMMMs) made of SMPs that can be used in different applications such as medicine, robotics, and lightweight structures. The origami structure made from SMP blends, activated with uniform heating. The effect of blend composition on the shape memory behavior was studied. Also the influence of the thermomechanical and the viscoelastic properties of origami unit cell on the activation process have been discussed, and stress relaxation and shape recovery were investigated. Activation process of the unit cell has been 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 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 score0.159
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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
Published2016
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Materials and MechanicsFrench-language works237,207