Automated insertion of package dies onto wire and into a textile yarn sheath
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.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".