MétaCan
Menu
Back to cohort
Record W3214802929 · doi:10.1002/app.51937

Extrusion‐based <scp>3D</scp> printing with high‐density polyethylene Birch‐fiber composites

2021· article· en· W3214802929 on OpenAlex
Agbelenko Koffi, Lotfi Toubal, Minde Jin, Demagna Koffi, Frank Döpper, Hans‐Werner Schmidt, Christian Neuber

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

VenueJournal of Applied Polymer Science · 2021
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHigh-density polyethyleneComposite materialMaterials sciencePolyolefinShrinkageComposite numberPolyethyleneExtrusionFiberWood flourDeformation (meteorology)

Abstract

fetched live from OpenAlex

Abstract High‐density polyethylene (HDPE) is one of the most widely used semi‐crystalline polyolefin thermoplastics. However, 3D printing with this material remains rare because of massive shrinkage and poor adhesion to common 3D printing build surfaces. In this study, shrinkage and warping were overcome by blending in short fibers of yellow birch at 10–30 wt% along with a coupling agent. Square tubes were printed to measure deformation and mechanical properties of this composite material. Deformation was reduced by 80% in material containing 30 wt% wood compared to neat HDPE. Young's modulus increased respectively by 25%, 30%, and 35% as the filler content increased to 10, 20, and 30 wt%. This is the first known successful 3D printing with wood‐fiber HDPE composite.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.690

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 science0.0010.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.198
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