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Record W7046766692

Effective Mechanical Properties of 3D Structural Metamaterials

2018· other· en· W7046766692 on OpenAlexaff

Bibliographic record

VenueYorkSpace (York University) · 2018
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsYork University
Fundersnot available
KeywordsMetamaterialScalingLattice (music)FabricationLattice constantMaterial properties
DOInot available

Abstract

fetched live from OpenAlex

Recent advances in fabrication techniques have enabled the creation of novel materials with enhanced mechanical properties commonly known as metamaterials. They refer to materials consisting of a unit cell tessellated in three orthogonal directions with dimensions scaling down to the nanoscale. The objective of this research was to describe the effective properties of the octet-truss unit cell at different lattice angles and loading directions. The research is composed of three consecutive parts. The first part addressed the analytical derivation of the continuum-based model while including the lattice angle. The second part demonstrated the impact of the lattice angle on its effective properties, namely relative density, effective stiffness, and effective strength. Finally, potential in lattice structure optimization is demonstrated through an experimental study. This work addressed optimizing the structural configuration of the octet-truss, which when combined with favorable size effects, would unlock the full potential of mechanical metamaterials as load-bearing structures.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 studies0.0000.001
Scholarly communication0.0010.000
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 head0.011
GPT teacher head0.211
Teacher spread0.200 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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