Direct-Ink-Writing Printed Strain Rosette Sensor Array with Optimized Circuit Layout
Bibliographic record
Abstract
Abstract The full-field multiaxial strain measurement is highly desired for application of structural monitoring but still challenging, especially when the manufacturing and assembling for large-area sensing devices is quite difficult. Compared with the traditional procedure of gluing commercial strain gauges on the structure surfaces for strain monitoring, the recently developed Direct-Ink-Writing (DIW) technology provides a feasible way to directly print sensors on the structure. However, there are still crucial issues in the design and printing strategies to be probed and improved. Therefore, in this work, we propose an integrated strategy from layered circuit scheme to rapid manufacturing of strain rosette sensor array based on the DIW technology. Benefit from the innovative design with simplified circuit layout and the advantages of DIW for printing multilayer structures, here we achieve optimization design principle for strain rosette sensor array with scalable circuit layout, which enable a hierarchical printing strategy for multiaxial strain monitoring in large scale or multiple domains. The strategy is highly expected to adapt for the emerging requirement in various applications such as integrated soft electronics, nondestructive testing and small-batch medical devices.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".