Unveiling the impact of short fibre reinforcement and extrusion properties on microstructure of 3D printed polycarbonate composites
Bibliographic record
Abstract
In the evolving realm of additive manufacturing, this study investigates the microstructural and mechanical implications of short fibre reinforcement within Polycarbonate (PC) composites fabricated via material extrusion (MEX). The research specifically examines the roles of extrusion temperature, extrusion multiplier and fibre content on void content and fibre alignment, with a focus on their influence on inter-bead strength and overall print quality. Through a combination of high-resolution micro-CT scanning and mechanical testing, the study reveals that an increase in the extrusion multiplier significantly enhances fibre-bridging up to 47 % and inter-bead adhesion up to 237 % depending on the fibre content. It also traces an optimal fibre content threshold that maximizes benefits of fibre bridging, thereby bolstering the mechanical properties of the material. The comprehensive analysis demonstrates that precise control over the extrusion parameters as well as filament quality are crucial for exploiting the full potential of fibre reinforcement in 3D printed structures. This research advances our understanding of MEX in fabricating short fibre-reinforced composites, offering novel insights for tailoring material properties to meet the demands of high-performance applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".