Manufacturing Strain Sensor Via Printed Electronics Onto 3D Printed Substrates
Bibliographic record
Abstract
Inkjet printing is a promising technology with advantages such as digital customization, reduced cost and fabrication time, and non-contact printing. These features enable inkjet printing to fabricate on novel, diverse substrates such as 3D printed substrates. 3D printing technology builds 3D structures with freedom of design, mass customization, low cost, and ability to fabricate complex geometries.\nIn this thesis, we aim to integrate inkjet printing with 3D printing technology and fabricate a strain gauge sensor. We use various additive manufacturing (AM) techniques including 3D fused filament fabrication (FFF) printing, extrusion printing, and inkjet printing. We used silver nanoparticle ink and studied different conditions to maximize electrical conductivity of the ink. This optimization includes ultraviolet (UV) time exposure, drop spacing, heating conditions, intense pulsed light parameters, and length shrinkage.\nA low-cost strain gauge sensor is fabricated onto 3D printed PLA material. The related mechanical/electrical tests are performed for the sensor characterization.
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 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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".