Stiffness prediction of 3D printed fiber-reinforced thermoplastic composites
Bibliographic record
Abstract
Purpose The purpose of this study is to confirm that the stiffness of fused filament fabrication (FFF) three-dimensionally (3D) printed fiber-reinforced thermoplastic (FRP) materials can be predicted using classical laminate theory (CLT), and to subsequently use the model to demonstrate its potential to improve the mechanical properties of FFF 3D printed parts intended for load-bearing applications. Design/methodology/approach The porosity and the fiber orientation in specimens printed with carbon fiber reinforced filament were calculated from micro-computed tomography (µCT) images. The infill portion of the sample was modeled using CLT, while the perimeter contour portion was modeled with a rule of mixtures (ROM) approach. Findings The µCT scan images showed that a low porosity of 0.7 ± 0.1% was achieved, and the fibers were highly oriented in the filament extrusion direction. CLT and ROM were effective analytical models to predict the elastic modulus and Poisson’s ratio of FFF 3D printed FRP laminates. Research limitations/implications In this study, the CLT model was only used to predict the properties of flat plates. Once the in-plane properties are known, however, they can be used in a finite element analysis to predict the behavior of plate and shell structures. Practical implications By controlling the raster orientation, the mechanical properties of a FFF part can be optimized for the intended application. Originality/value Before this study, CLT had not been validated for FFF 3D printed FRPs. CLT can be used to help designers tailor the raster pattern of each layer for specific stiffness requirements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".