Holographic direct sound printing
Bibliographic record
Abstract
Direct sound printing (DSP), an alternative additive manufacturing process driven by sonochemical polymerization, has traditionally been confined to a single acoustic focal region, resulting in a voxel-by-voxel printing approach. To overcome this limitation, we introduce holographic direct sound printing (HDSP), where acoustic holograms, storing cross-sectional images of the desired parts, pattern acoustic waves to induce regional cavitation bubbles and on-demand regional polymerization. HDSP outperforms DSP in terms of printing speed by one order of magnitude and yields layerless printed structures. In our HDSP implementation, the hologram remains stationary while the printing platform moves along a three-dimensional path using a robotic arm. We present sono-chemiluminescence and high-speed imaging experiments to thoroughly investigate HDSP and demonstrate its versatility in applications such as remote ex-vivo in-body printing and complex robot trajectory planning. We showcase multi-object and multi-material printing and provide a comprehensive process characterization, including the effects of hologram design and manufacturing on the HDSP process, polymerization progression tracking, porosity tuning, and robotic trajectory computation. Our HDSP method establishes the integration of acoustic holography in DSP and related applications. Within the field of additive manufacturing, direct sound printing is limited to a voxel-by-voxel printing. Here, the authors overcome this limitation with a holographic direct sound printing technique in which the information printed part is stored in an acoustic hologram and the printing material polymerizes instantly therefore improving the printing speed and allowing to print multimaterial objects even behind optically non-transparent media.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".