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Record W4285794823 · doi:10.1002/cjce.24562

Mechanical properties of thermoplastic polymers in fused filament fabrication ( <scp>FFF</scp> )

2022· article· en· W4285794823 on OpenAlexafffundvenue
Nancy Bhardwaj, H. Henein, Tonya Wolfe

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsRed Deer PolytechnicUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFused filament fabricationMaterials scienceUltimate tensile strengthThermoplasticFabricationDuctility (Earth science)Composite materialPolymerProtein filamentModulus3D printingRaw material

Abstract

fetched live from OpenAlex

Abstract Fused filament fabrication (FFF) is a method used to fabricate initial prototypes out of polymeric materials, with printed parts often being used as load‐carrying elements. The mechanical properties of various polymer materials produced under certain printing conditions exhibit superior strength and ductility and may serve as an inexpensive replacement for metal or wooden parts. To gain knowledge of 3D‐printed part strength and compare the quality of filament material from several manufacturers, this study characterizes the mechanical properties of parts printed from various commercial FFF feedstocks. Uniaxial tensile testing is performed to determine the ultimate tensile strength, Young's modulus, and fracture strain of polymer and composite materials. This study, thus, demonstrates the importance and necessity of evaluating the filament feedstock prior to printing, as polymeric filaments of the same type of polymer can have different mechanical properties.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.348

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 integrity0.0000.001
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 head0.011
GPT teacher head0.164
Teacher spread0.153 · 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 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

Citations7
Published2022
Admission routes3
Has abstractyes

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