Machine Learning Insights Into the Mechanical Behavior of Fused Filament Fabricated Polylactic Acid Composites Reinforced With Carbon Fiber, Graphene, and Multi‐Walled Carbon Nano Tubes
Bibliographic record
Abstract
ABSTRACT Polylactic acid (PLA) is widely used in fused filament fabrication (FFF) due to its biocompatibility and low cost, but its inherent brittleness and limited mechanical strength restrict its application in load‐bearing and structural components. Reinforcing PLA with nanofillers such as carbon fiber (CF), graphene (Gr), and multi‐walled carbon nanotubes (MWCNTs) offers a promising route to overcome these limitations, yet the combined influence of build parameters and reinforcement type remains underexplored. In this study, PLA composites were fabricated via FFF with systematic variation of raster orientation (RO), layer height (LH), and print speed (PS), and their tensile, compressive, and flexural properties were evaluated. Among the reinforcements, PLA‐MWCNT composites demonstrated the highest compressive strength (121.25 MPa at 0° RO, 0.1 mm LH, 30 mm/s PS), while PLA‐CF composites exhibited superior tensile performance, and PLA‐Gr composites offered balanced mechanical behavior. To predict and optimize these outcomes, machine learning (ML) models—Linear Regression, Random Forest Regression, and Extreme Gradient Boosting Regression—were employed, with XGBR achieving the highest accuracy ( R 2 up to 0.99). This integrated experimental–computational framework identifies reinforcement–parameter combinations most suitable for real‐world applications such as lightweight automotive components while also highlighting the need for larger datasets to further strengthen ML‐driven predictions.
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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) | 0.000 | 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".