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Record W3135045720 · doi:10.3390/applmech2010006

Conformal Wireframe Nets for Trimmed Symmetric Unit Cells in Functionally Graded Lattice Materials

2021· article· en· W3135045720 on OpenAlexafffund
Eric Trudel, Mostafa S. A. ElSayed

Bibliographic record

VenueApplied Mechanics · 2021
Typearticle
Languageen
FieldEngineering
TopicTopology Optimization in Engineering
Canadian institutionsCarleton University
FundersMitacsBombardier
KeywordsTrussLattice (music)Conformal mapBravais latticeCubic crystal systemTopology (electrical circuits)Diamond cubicMathematicsGeometryCrystal structureComputer scienceMaterials sciencePhysicsDiamondCombinatoricsCondensed matter physicsStructural engineeringEngineeringCrystallographyComposite material

Abstract

fetched live from OpenAlex

Tessellating a periodic unit cell of lattice material to fill a design space in complex geometries has many challenges arising from their computer-aided design (CAD) modeling intricacy. A solution to this difficulty is the use of trimmed micro-truss lattice structures with a conformal net. This paper presents a novel algorithm for constructing conformal lattice net as wireframe of one-dimensional line segments suitable for Bravais cubic symmetric truss-based topologies. The novel algorithm is an excellent candidate when dealing with lattice structures using cubic, body-centered cubic (BCC), face-centered cubic (FCC), and/or diamond unit cell configurations. The wireframe structure is easily transferred into one-dimensional beam elements for microscale optimizations to obtain a functionally graded lattice material. It is shown that introduction of the lattice net resulted in a significant reduction in the mass of the optimized design.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.204
Teacher spread0.191 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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