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Record W2901478105 · doi:10.25071/10315/35407

Collapse Surfaces Of The Octet-Truss Lattice At Different Lattice Angles

2018· article· en· W2901478105 on OpenAlexaff
Mohamed Abdelhamid, Czekanski Aleksander

Bibliographic record

VenueProgress in Canadian Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsYork University
Fundersnot available
KeywordsTrussOctetLattice (music)Materials scienceCondensed matter physicsPhysicsStructural engineeringEngineeringNuclear physicsAcoustics

Abstract

fetched live from OpenAlex

Cellular materials are found extensively in nature such as wood, honeycomb, butterfly wings and foam-like structures like trabecular bone and sponge. This class of materials proves to be structurally-efficient by combining low weight with superior mechanical properties. Recent studies have shown there are coupling relations between the mechanical properties of cellular materials and their relative density. Due to its favorable stretching-dominated behavior, continuum models of the octet-truss were developed to describe its effective mechanical properties. However, previous studies were only performed for the cubic symmetry case where the lattice angle = 45. In this work, we study the impact of the lattice angle on the effective strength of the octet-truss. A graphical method is utilized to plot the collapse surfaces for plastic yielding and elastic buckling for different loading combinations at different lattice angles. Generally, the ( , ) loading space is preferable for lattice angles greater than 45, while the ( , ) loading space is preferable for lattice angles lower than 45.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.925

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 head0.006
GPT teacher head0.199
Teacher spread0.193 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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