Computational Design of Active 3D-Printed Multi-State Structures for\n Shape Morphing
Bibliographic record
Abstract
Active structures have the ability to change their shape, properties, and\nfunctionality as a response to changing operational conditions, which makes\nthem more versatile than their static counterparts. However, most active\nstructures currently lack the capability to achieve multiple, different target\nstates with a single input actuation or require a tedious material programming\nstep. Furthermore, the systematic design and fabrication of active structures\nis still a challenge as many structures are designed by hand in a trial and\nerror process and thus are limited by engineers' knowledge and experience. In\nthis work, a computational design and fabrication framework is proposed to\ngenerate structures with multiple target states for one input actuation that\ndon't require a separate training step. A material dithering scheme based on\nmulti-material 3D printing is combined with locally applied copper coil heating\nelements and sequential heating patterns to control the thermo-mechanical\nproperties of the structures and switch between the different deformation\nmodes. A novel topology optimization approach based on power diagrams is used\nto encode the different target states in the structure while ensuring the\nfabricability of the structures and the compatibility with the drop-in heating\nelements. The versatility of the proposed framework is demonstrated for four\ndifferent example structures from engineering and computer graphics. The\nnumerical and experimental results show that the optimization framework can\nproduce structures that show the desired motion, but experimental accuracy is\nlimited by current fabrication methods. The generality of the proposed method\nmakes it suitable for the development of structures for applications in many\ndifferent fields from aerospace to robotics to animated fabrication in computer\ngraphics.\n
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".