Printing of low-viscosity materials using tomographic additive manufacturing
Bibliographic record
Abstract
Tomographic volumetric additive manufacturing (VAM) is a high-speed 3D printing technique that overcomes many of the challenges faced by conventional layer-by-layer based approaches. However, unlike other vat photopolymerization techniques, VAM must use much higher viscosity resins prohibiting the use of more commonly available lower-viscosity materials. Low-viscosity poly(ethylene glycol) diacrylate (PEGDA) has seen wide usage in bioprinting techniques but has eluded printing in VAM. Using a VAM printer with a high angular dose delivery rate, as well as tomographic projections optimized for low-viscosity printing conditions, we demonstrate high-fidelity VAM printing in PEGDA with viscosities as low as 12 cP. Micro-computed tomography imaging of printed parts reveal close-to voxel resolution limited performance. Furthermore, we have demonstrated the first direct printing of a low-viscosity hydrogel in VAM. The proposed method expands the viscosity range, and in turn the catalogue of materials accessible to VAM, giving this printing modality the broadest viscosity range of any vat photopolymerization technique.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".