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Record W2632955854 · doi:10.1080/20550340.2017.1341125

Characterizing short-fiber-reinforced composites produced using additive manufacturing

2017· article· en· W2632955854 on OpenAlex

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

VenueAdvanced Manufacturing Polymer & Composites Science · 2017
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Alberta
FundersKowa Company
KeywordsMaterials scienceComposite materialPolylactic acidCrystallinityUltimate tensile strengthExtrusionAnnealing (glass)Protein filamentTensile testingFused filament fabricationYoung's modulusMicrostructurePolymerElastic modulus

Abstract

fetched live from OpenAlex

Material extrusion additive manufacturing (MEAM), a sub-branch of three-dimensional (3D) printing is growing in popularity. Test specimens were 3D-printed using commercial polylactic acid (PLA) filament, and PLA filament reinforced with short-carbon fibers (PLA/CF). As-printed specimens and specimens that were annealed at three different temperatures, then subjected to tensile testing. The internal microstructures of the samples were also examined. The effects of the short-carbon fiber fillers on the mechanical properties of 3D-printed PLA were investigated, and the effects of the annealing process on polymer crystallinity and mechanical properties. The annealing process was shown to increase the crystallinity of both sample groups, though no statistically significant effect of annealing on mechanical properties was observed. The tensile properties of the PLA and PLA/CF filaments showed that the addition of carbon fibers to the PLA filament led to a significant increase in elastic modulus of the MEAM samples.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
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 score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0010.003
Open science0.0030.001
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 head0.020
GPT teacher head0.259
Teacher spread0.240 · 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