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Record W4379539188 · doi:10.48550/arxiv.2306.02145

Origami-Inspired Composite Springs with Bi-directional Translational-Rotational Functionalities

2023· preprint· en· W4379539188 on OpenAlexfundno aff
Ravindra Masana, Mohammed F. Daqaq

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsnot available
FundersYork UniversityNew York University Abu Dhabi
KeywordsStiffnessKinematicsSpring (device)Translation (biology)Composite numberComputer scienceBiomimetics3D printingMechanical engineeringNanotechnologyTopology (electrical circuits)EngineeringMaterials scienceStructural engineeringPhysicsClassical mechanicsAlgorithm

Abstract

fetched live from OpenAlex

Many of the patterns seen in Origami are currently being explored as a platform for building functional engineering systems with versatile characteristics that cater to niche applications in various technological fields. One such pattern is the Kresling pattern, which offers unconventional mechanical properties with rich coupled translation and rotational kinematics. In this paper, we design and manufacture a composite spring inspired by the Kresling Origami pattern, which is capable of simultaneously behaving as an axial and torsional restoring element with bi-directional functionalities. We study, numerically and experimentally, the restoring behavior of this spring, its equilibria, and their bifurcations for different combination of the design parameters. We show that the fabricated springs can have fixed, quasi-zero, or variable stiffness, and can be customized to exhibit single- or multi-stable states (symmetric and asymmetric) as needed. The proposed spring demonstrates how combining additive manufacturing with Origami principles can offer a new pathway towards the design of new structural and machine elements with versatile functionalities.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
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 head0.059
GPT teacher head0.170
Teacher spread0.111 · 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 designSimulation or modeling
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

Citations4
Published2023
Admission routes1
Has abstractyes

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