MétaCan
Menu
Back to cohort

Four-Dimensional Printing on Textiles Evaluating Digital File-to-Fabrication Workflows for Self-Forming Composite Shell Structures

2023· article· en· W4386814890 on OpenAlexaff
Asterios Agkathidis, David Jourdan, Yang Song, Arathi Kanmani, Ansha Thomas

Bibliographic record

VenueeCAADe proceedings · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsASTER
Fundersnot available
KeywordsShell (structure)FabricationWorkflowComputer science3D printingEngineering drawingMaterials scienceComposite numberFused filament fabricationMechanical engineeringTable (database)TextileStress (linguistics)Composite materialEngineeringDatabase

Abstract

fetched live from OpenAlex

This design-led research investigates the development of self-forming wearable composite structures by printing embossed patterns out of flexible filament on pre-stretched textiles and releasing the stress after the printing has been completed, whereby time becomes the fourth dimension of the printing process. In particular, the study presents and compares three methods of ‘file-to-fabrication’ techniques for generating self-forming textile shell structures: The first is based on modified geometrical patterns in relation to curvature analysis, the second on printed patterns related to their stress line simulation and the third on an analysis of the anisotropic shrinking behaviour of stripe patterns. The findings emphasize the advantages and challenges of each method as well as present a comparative table chart highlighting the relationship between material properties, pattern geometry and the formal vocabulary of the composite shells.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.265
Teacher spread0.239 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueeCAADe proceedingsSame topicAdvanced Materials and MechanicsFrench-language works237,207