Fabricating 3D Structures by Combining 2D Printing and Relaxation of Strain
Bibliographic record
Abstract
Abstract This paper describes the fabrication of elastomeric three‐dimensional (3D) structures starting from two‐dimensional (2D) sheets using a combination of direct‐ink printing and relaxation of strain. These structures are fabricated in a two‐step process: first, elastomeric inks are deposited as 2D structures on a stretched elastomeric sheet, and second, after curing of the elastomeric inks, relaxation of strain in the 2D sheet causes it to deform into a 3D shape. To predict bending of elastomeric objects fabricated with this technique, a simple mechanical model is developed. The strategy of using initially 2D materials to fabricate 3D structures offers four new features that complement digital fabrication techniques. (i) It provides a simple route to create shapes with complex curves, suspended features, and internal cavities. (ii) It is a faster method of fabricating some types of shapes than “conventional” 3D printing, because the features are printed in 2D. (iii) It forms surfaces that can be both smoother, and structured in a way that is not compatible with layer‐by‐layer processing. (iv) It forms structures that can be deformed reversibly after fabrication by reapplying strain. This paper demonstrates these features by fabrication of helices, structures inspired by cubes and tables, “pop‐up” structures, and soft grippers.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".