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Record W4410233045 · doi:10.1016/j.addlet.2025.100289

Continuous carbon fiber 3D printing with interweaving deposition for enhanced mechanical performance in fused filament fabrication

2025· article· en· W4410233045 on OpenAlexafffund
Ahmed El-Sherbiny, Ahmed Jawad Qureshi, Pierre Mertiny

Bibliographic record

VenueAdditive Manufacturing Letters · 2025
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFabrication3D printingDeposition (geology)Materials scienceFused filament fabricationProtein filamentFiberNanotechnologyFused deposition modelingCarbon fibersComposite materialGeologyComposite number

Abstract

fetched live from OpenAlex

Additive manufacturing (AM) has revolutionized modern manufacturing by enabling the rapid prototyping and production of complex geometries with minimal material waste. Among AM techniques, Fused Filament Fabrication (FFF) is widely used for polymer-based manufacturing but exhibits limitations in high-performance applications due to insufficient mechanical properties. To address these shortcomings, this study focuses on advancing a standard FFF system to integrate Continuous Carbon Fiber (CCF) and implement interweaving deposition patterns, with the goal of enhancing structural performance and integrity. Representative volume element modeling and finite element analysis were conducted to evaluate the mechanical behavior, with results validated through experimental mechanical testing. The results confirm that CCF reinforcement improves the mechanical performance of printed components, particularly in the raster direction, although variations in response highlight the influence of material imperfections and non-linearities. The study demonstrates the potential of advanced CCF 3D printing in addressing the limitations of traditional FFF and provides insights for further advancements in polymer composite AM.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
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
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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.

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

Citations3
Published2025
Admission routes2
Has abstractyes

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