Making automation pay - cost & throughput trade-offs in the manufacture of large composite components
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
The automation of complex manufacturing operations can provide significant savings over manual processes, and there remains much scope for increasing automation in the production of large scale structural composites. However the relationships between driving variables are complex, and the achievable throughput rate and corresponding cost for a given design are often not apparent. The deposition rate, number of machines required and unit production rates needed are interrelated and consequently the optimum unit cost is difficult to predict. A detailed study of the costs involved for a series of composite wing cover panels with different manufacturing requirements was undertaken. Panels were sized to account for manufacturing requirements and structural load requirements allowing both manual and automated lay-up procedures to influence design. It was discovered that the introduction of automated tape lay-up can significantly reduce material unit cost, and improve material utilisation, however higher production rates are needed to see this benefit.
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 | 0.001 |
| 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