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Record W2975439048 · doi:10.1088/1361-665x/ab47c9

In-plane compression behavior of anti-tetrachiral and re-entrant lattices

2019· article· en· W2975439048 on OpenAlexaff
Kadir Günaydın, Zana Eren, Zafer Kazancı, Fabrizio Scarpa, Antonio Mattia Grande, Halit S. Türkmen

Bibliographic record

VenueSmart Materials and Structures · 2019
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsAuxeticsMaterials scienceFinite element methodDeformation (meteorology)Nonlinear systemLattice (music)Deformation mechanismBucklingStructural engineeringHexagonal crystal systemComposite materialPhysicsCrystallographyEngineering

Abstract

fetched live from OpenAlex

Abstract In the present study, a comparative compression investigation of anti-tetrachiral and modified re-entrant lattices was conducted in-plane direction using experimental and numerical analyses. Lattice structures were manufactured using fused deposition modelling 3D printing technology and crushed at quasi-static condition. Nonlinear finite element (FE) models of both structures were established, and the FE results were systematically compared with the experimental results. The onset of densification phases of both structures was determined numerically. Results indicate that deformation modes strongly affect the force-deflection response of both designs. In this manner, failure locations and buckling deformation in the tests were identified to find a relation with theory and to modify geometries. The anti-tetrachiral design exhibits higher specific energy absorption than modified re-entrant hexagonal lattices. Beyond the auxetic characteristics, deformation mechanism of the anti-tetrachiral lattices provides an opportunity to construct excellent crush absorption in-plane direction thanks to its high shear strength stem from its unique deformation mechanism.

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 score0.003

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 head0.005
GPT teacher head0.207
Teacher spread0.203 · 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

Citations59
Published2019
Admission routes1
Has abstractyes

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