A Curvature-Tunable Deployable Origami Boom With Facet-Integrated Self-Locking Mechanism
Bibliographic record
Abstract
Abstract Recent advances in technology have expanded the space industry, but there are still volume limitations on carriers, which translates directly into cost. Deployable structures can overcome these limitations and are used especially in space in a variety of ways. In this article, this study proposes a curvature-adjustable origami boom incorporating a plane-induced based self-locking mechanism. The Kirigami locker, which deploys with the pattern and is self-locking, can increase rigidity while minimizing the increase in storage volume. By utilizing the characteristics of the Miura pattern, the results show a difference in compressive and bending stiffness of up to 6.29 and 3.5 times, respectively, with and without the locking segment. In addition, the curvature can be freely designed through pattern variation, and booms with multiple curvatures can be produced. This enables the design of a variety of highly rigid and deployable structures, ranging from small sizes such as tables to large structures, including shelters and masts, which can be deployed with few degrees-of-freedom.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 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".