Four-Dimensional Multistep Vertical Printing for Hygroresponsive Shape Change with Nonplanar Rest-State Geometries
Bibliographic record
Abstract
Four-dimensional printing (4DP) via fused deposition modeling has been used to create hygromorphic biocomposite actuators through wood polymer composite (WPC) filaments. The shape-change transformation of the 4DP composite mechanism is preprogrammed by controlling the printing process parameters and the design of the print-path pattern. Until now, most 4DP approaches involving Wood Polymer Composite (WPCs) have focused on planar actuators featuring a bilayer structure composed of laminar layers with distinct material properties. These mechanisms show a laminar initial rest state, presenting as flat objects, and can only achieve a complex three-dimensional shape when subjected to the moisture variations stimulus. The presented research highlights the development of a multistage printing method that expands the capabilities of three-axis printers to enable the 4DP of mechanism with complex nonplanar rest-state geometries. The new technical capabilities of this method are demonstrated here through the creation and testing of novel nonlaminar 4DP mechanisms that harness their unique doubly curved rest-state geometry to achieve kinematic amplification. We expect that this approach can greatly improve the range and complexity of 4DP mechanisms that can be developed using the commonly available three-axis printers.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".