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Record W4381185307 · doi:10.1017/pds.2023.205

GEOMETRIC MODELLING OF HETEROGENEOUS LATTICE STRUCTURES THROUGH FUNCTION REPRESENTATION WITH LATTICEQUERY

2023· article· en· W4381185307 on OpenAlex
Nikita Letov, Yaoyao Fiona Zhao

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings of the Design Society · 2023
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsMcGill University
FundersMcGill University
KeywordsLattice (music)Geometric modelingComputer scienceRepresentation (politics)PersonalizationGeometric designTheoretical computer scienceMathematicsGeometryPhysics

Abstract

fetched live from OpenAlex

Abstract Lattice structures are lightweight and possess other unique mechanical and physical properties. Additive manufacturing techniques are often used to produce these structures. Additive manufacturing provides manufacturing freedom that significantly surpasses the one provided by conventional subtractive manufacturing. However, a gap exists between the manufacturing freedom and the geometric modelling freedom in additive manufacturing: it can be extremely challenging to model the designed part because of its high geometric complexity, such as heterogeneous lattice structures. While several tools on the market allow geometric modelling of such structures available on the market, the customization of lattice parameters can still be significantly improved. Moreover, no open-source tools exist to address this issue or to model lattice structures in general. This work presents a novel open-source library for the geometric modelling of lattice structures with customized parameters. The parameter customization is enabled with the function representation approach.

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.

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 score0.860
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.059
GPT teacher head0.234
Teacher spread0.175 · 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