Automated insertion of package dies onto wire and into a textile yarn sheath
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Wider adoption of electronic textiles requires integration of small electronic components into textile fabrics, without comprising the textile qualities. A solution is to create a flexible yarn that incorporates electronic components within the fibres of the yarn (E-yarn). The production of these novel E-yarns was initially a craft skill, with the inclusion of package dies within the fibres of the yarn taking about 90 min. The research described here demonstrated that it is possible to produce E-yarns on an industrial scale by automating the manufacturing process. This involved adapting printed circuit board manufacturing technology and textile yarn covering machinery. The production process started with re-flow soldering of package dies onto fine multi-strand copper wire. A carrier yarn was then placed in parallel with the copper wire to provide tensile strength. The package die and adjacent carrier yarn were then encapsulated in a polymer micro-pod to provide protection from moisture ingress and from mechanical strain on the die and solder joints. The process was then completed by surrounding the micro-pod and copper interconnects with additional fibres, held tightly together with a knitted fibre-sheath. This prototype, automated production process reduced the time for embedding one micro-device within a yarn to 6 min, thus increasing the production speed, demonstrating that automation of the E-yarn production process is feasible. Prototype garments have been created using E- yarns. Further developments can include automated transfer of the yarn components from one stage of production to the next, enabling greater increases in speed of manufacture of E yarns.
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.
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