Advantages of in-situ impregnation 3D printing in continuous flax/PLA biocomposites for optimized mechanical properties in structural applications
Bibliographic record
Abstract
The in-situ impregnation 3D printing technique offers a unique advantage over traditional composite manufacturing methods: the ability to control fiber weight fraction within a single print. This study demonstrates this capability through the printing of continuous flax fiber-reinforced PLA biocomposites. A parametric study was conducted to determine the optimal printing parameters, including nozzle temperature and cooling fan usage. Subsequently, samples with four different fiber weight fractions (0, 30, 40, and 50 wt%) were printed using the optimized parameters. Tensile properties were evaluated through both tensile testing and acoustic impulse measurements, while Shore D hardness, density and porosity were also assessed. The results reveal a significant improvement in tensile properties and an increase in density with increasing fiber weight fraction, accompanied by a decrease in Shore D hardness. Additionally, the choice of printing pattern, influences the tensile properties and damage mechanisms. These findings highlight the potential of in situ impregnation for producing custom fiber-reinforced biocomposites with tailored mechanical properties at a low cost.
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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".