Automatic Exposure Volumetric Additive Manufacturing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Tomographic volumetric additive manufacturing (VAM) achieves high print speed and design freedom by continuous volumetric light patterning. This differs from traditional vat photopolymerization techniques that use brief sequential (2D) plane‐ or (1D) point‐localized exposures. The drawback to volumetric light patterning is the small exposure window. Overexposure quickly leads to cured out‐of‐part voxels due to the nonzero background dose arising from light projection through the build volume. For tomographic VAM, correct exposure time is critical to achieving high repeatability, however, we found that correct exposure time varies by ≈40% depending on resin history. Currently, tomographic VAM exposure is timed based on subjective human determination of print completion, which is tedious and yields poor repeatability. Here, a robust auto‐exposure routine is implemented for tomographic VAM using real‐time processing of light scattering data, yielding accurate and repeatable prints without human intervention. The resulting print fidelity and repeatability approaches, and in some cases, exceeds that of commercial resin 3D printers. It is shown that auto‐exposure VAM generalizes well to a wide variety of print geometries with small positive and negative features. The repeatability and accuracy of auto exposure VAM allows for building multi‐part objects, fulfilling a major requirement of additive manufacturing technologies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it