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Record W4282927932 · doi:10.31276/vjste.64(2).34-41

V.I.E.T.N.A.M. by 4D printing of composites

2022· article· en· W4282927932 on OpenAlex
Suong V. Hoa, Daniel Rosca

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.

Bibliographic record

VenueMinistry of Science and Technology Vietnam · 2022
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsComposite materialEpoxyMaterials scienceAnisotropyCuring (chemistry)Fused deposition modelingComposite numberEngineering drawing3D printingComputer scienceEngineeringPhysicsOptics

Abstract

fetched live from OpenAlex

This paper presents an application of 4D printing of composites (4DPC) to make composite structures of complex geometries without the need for complex moulds. This application is illustrated through the formations of the letters V, i, e, t, n, a, and m, which form the word Vietnam. In the procedure, laminates made of carbon/epoxy prepregs are laid on a flat mould. The deposition of the prepregs on the flat mould is done using an automated fibre placement machine (AFP), which can be considered as a large size 3D printer. For a smaller structure, the prepreg deposition can be accomplished using an AFP machine or by hand lay-up. Upon curing and cooling to room temperature, the laminate transforms itself from a flat configuration to the shape of the intended letter, except for the letter V. The mechanism that enables this transformation relies on the anisotropy of the laminate. This method has many potential applications, particularly in the delivery of bulky three-dimensional structures to remote locations.

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 categoriesScience and technology studies
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 score0.113
Threshold uncertainty score1.000

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 studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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