Conformal Geometry and Multimaterial Additive Manufacturing through Freeform Transformation of Building Layers
Bibliographic record
Abstract
3D printing, formally known as additive manufacturing, creates complex geometries via layer-by-layer addition of materials. While 3D printing has been historically perceived as the static addition of build layers, 3D printing is now considered as a dynamic assembly process. In this context, here a new 3D printing process is reported that executes full degree-of-freedom (DOF) transformation (translating, rotating, and scaling) of each individual building layer while utilizing continuous fabrication techniques. Transforming individual building layers within the sequential layered manufacturing process enables dynamic transformation of the 3D printed parts on-the-fly, eliminating the time-consuming redesign steps. Preserving the locality of the transformation to each layer further enables the discrete conformal transformation, allowing objects such as vascular scaffolds to be optimally fabricated to properly fit within specific patient anatomy obtained from the magnetic resonance imaging (MRI) measurements. Finally, exploiting the freedom to control the orientation of each individual building layer, multimaterials, multiaxis 3D printing capability are further established for integrating functional modules made of dissimilar materials in 3D printed devices. This final capability is demonstrated through 3D printing a soft pneumatic gripper via heterogenous integration of rigid base and soft actuating limbs.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".