Precise fiber alignment in stereolithography (SLA) 3D printing of composite polymers
Bibliographic record
Abstract
Additive manufacturing (AM) has advanced significantly, yet challenges remain in producing composites with tailored properties. Stereolithography (SLA), a high-resolution AM technique, struggles to achieve controlled fiber orientation in composite materials. This study addresses this limitation by integrating an in-house electromagnetic filler alignment system into a commercial SLA 3D printer. The system uses electromagnets to align reinforcing fillers at 0° and 90° during printing. Acrylic resin-cobalt powder composites were fabricated and analyzed using optical microscopy, tensile testing, micro-indentation, and scanning electron microscopy (SEM). Microscopy confirmed successful fiber alignment with the electromagnet system. Compared to the control (pure resin) and randomly oriented samples, the aligned composites exhibited lower stiffness but significantly enhanced ductility. Specifically, the strain at failure increased from 1.4% in the control samples to 7.9% and 6.8% in the 0° (perpendicular to loading direction) and 90° (parallel to loading direction) aligned composites, respectively. This marked improvement in strain capacity indicates a clear transition to more ductile behavior, a trend further corroborated by SEM observations. This approach overcomes SLA limitations, enabling controlled filler alignment for enhanced mechanical, thermal, and electrical properties. These advancements hold promise for customized manufacturing in aerospace, automotive, medical, and computing industries.
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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".