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Extrusion optimization and advanced mechanical characterization of fibre-reinforced polycarbonate filaments: Improving performance for fused filament fabrication

2025· article· en· W4406852542 on OpenAlexafffund
Farimah Tikhani, Pascal Hubert

Bibliographic record

VenueComposites Part A Applied Science and Manufacturing · 2025
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsMcGill UniversityAS Composite (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaFaculty of Engineering, McGill University
KeywordsFused filament fabricationPolycarbonateMaterials scienceExtrusionFabricationComposite materialProtein filamentCharacterization (materials science)Plastics extrusionPolymerNanotechnology

Abstract

fetched live from OpenAlex

This study explores the optimization of extrusion process parameters for fabrication of short glass fibre-reinforced polycarbonate filaments suitable for Fused Filament Fabrication. Employing Response Surface Methodology, the effects of fibre content, screw speed, and die temperature on mechanical properties and dimensional stability of filaments were investigated. This work introduces the application of Digital Image Correlation directly on filaments during tensile testing, contributing to the development of advanced filament characterization techniques. The optimal parameters—10 wt% fibre content, 40 rpm screw speed, and 239.9 °C die temperature—achieved a balance between high tensile modulus, high tensile strength, and minimal diameter deviation. The optimal processing condition led to a 148 % increase in tensile modulus, while maintaining good tensile strength and acceptable diameter deviation. Overall, fibre content had the most significant impact on filament properties, followed by screw speed. Fracture surface analysis provided valuable insights about the effects of process parameters on microstructure of the composites.

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 score0.286
Threshold uncertainty score0.594

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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
Published2025
Admission routes2
Has abstractyes

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