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Design of Load Path-oriented BCCz Lattice Sandwich Structures

2022· article· en· W4224234369 on OpenAlexaff
Shengjie Zhao, Xinxiang Zong, Nan Wu

Bibliographic record

VenueJournal of Physics Conference Series · 2022
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLattice (music)Homogenization (climate)CantileverStiffnessCrystal structureStructural engineeringMaterials scienceReinforcementComposite materialPhysicsEngineeringCrystallography

Abstract

fetched live from OpenAlex

Abstract Lattice structures are increasingly used in lightweight designs due to the advances of additive manufacturing. The overall performance of the lattice structures highly depends on the lattice cell arrangement. Previous studies show that body-centered cubic with z-axis reinforcement (BCCz) lattice has higher stiffness and strength compared to regular body-centered cubic (BCC) lattice subject to unidirectional compression. In this report, a load path-based methodology for the design of BCCz lattice sandwich structure with variable reinforcement directions is presented. A homogenization model of the sandwich structure with a specified volume fraction is developed first. Load path analysis is then conducted on the homogenization model to calculate the pointing stress vectors, which are lastly used to determine the orientations of BCCz cells. Based on the numerical simulations of a cantilever sandwich structure, the proposed lattice design has superior specific stiffness over the designs based on BCC or uniformly oriented BCCz unit cells.

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 score0.329
Threshold uncertainty score0.520

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 head0.013
GPT teacher head0.206
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 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
Published2022
Admission routes1
Has abstractyes

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