Blackbox optimization for origami-inspired bistable structures
Why this work is in the frame
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Bibliographic record
Abstract
Bistable mechanical systems exhibit two stable configurations where the elastic energy is locally minimized. To realize such systems, origami techniques have been proposed as a versatile platform to design deployable structures with both compact and functional stable states. Conceptually, a bistable origami motif is composed of two-dimensional surfaces connected by one-dimensional fold lines. This leads to stable configurations exhibiting zero-energy local minima. Physically, origami-inspired structures are three-dimensional, comprising facets and hinges fabricated in a distinct stable state where residual stresses are minimized. This leads to the dominance of one stable state over the other. To improve mechanical performance, one can solve the constrained optimization problem of maximizing the bistability of origami structures, defined as the amount of elastic energy required to switch between stable states, while ensuring materials used for the facets and hinges remain within their elastic regime. In this study, the Mesh Adaptive Direct Search (Mads) algorithm, a blackbox optimization technique, is used to solve the constrained optimization problem. The bistable waterbomb-base origami motif is selected as a case-study to present the methodology. The elastic energy of this origami pattern under deployment is calculated via Finite Element simulations which serve as the blackbox in the Mads optimization loop. To validate the results, optimized waterbomb-base geometries are built via Fused Filament Fabrication and their response under loading is characterized experimentally on a Uniaxial Test Machine. Ultimately, our method offers a general framework for optimizing bistability in mechanical systems, presenting opportunities for advancement across various engineering applications.
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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) | 0.000 | 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) | 0.000 | 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 it