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Record W4403317977 · doi:10.1145/3672539.3686743

Computational Design and Fabrication of 3D Printed Zippers Connecting 3D Textile Structures

2024· article· en· W4403317977 on OpenAlexaff
Ryo Masuda, Yuta Noma, Koya Narumi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFabrication3d printedTextile3D printingComputer scienceEngineering drawingEngineeringMechanical engineeringMaterials scienceManufacturing engineeringComposite material

Abstract

fetched live from OpenAlex

Zippers have long been used for connecting and disconnecting two textiles repeatedly and easily. However, many industrial zippers cannot retain their shape after assembly, limiting the potential use of connecting soft textiles to form 3D shapes with large and various curvatures. Thus, we present a method to design and fabricate interlocking 3D printable zippers that can encode post-assembly shapes. The user first gives a target 3D shape divided into developable patches (i.e., curved surfaces unrollable like paper). Then, our design software built on Rhino/Grasshopper computes an interlocking zipper on the boundary curve of the patches. The user 3D prints the zipper in a flat state and welds it to the edge of the textiles by thermal bonding, which can zip into a target 3D shape. In this demo, we report our method and exhibit design examples of 3D printed zippers.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.575
Threshold uncertainty score0.264

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 head0.016
GPT teacher head0.237
Teacher spread0.220 · 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 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

Citations6
Published2024
Admission routes1
Has abstractyes

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