Advances in a next generation measurement & inspection system for automated fibre placement
Bibliographic record
Abstract
Achieving the high production rates promised by automated fibre placement (AFP) is often hindered by lengthy and variable manual intervention, predominantly 100% visual inspection of every deposited ply; introducing machine stoppages, the risk of quality escapes, and the cost overhead of employing a manual inspection system with low fidelity measurement aids. An in-situ inspection system embedded in the manufacturing process is a vital enabler to achieve world class manufacturing performance. This paper describes a new and innovative measurement solution based on an optical, interferometric imaging technique called optical coherence tomography (OCT). The system’s characteristics facilitate the design of a compact probe which can be easily integrated onto the AFP head and allow measurements very close to the compaction roller nip point. The high fidelity system shows little sensitivity to differences in material reflectivity and sensor to part incident angle. This paper will cover the initial implementation and system demonstration in a TRL 6 production mature environment using a Viper AFP machine from Fives-Cincinnati. Results will demonstrate the ability of this novel inspection system to accurately detect and measure defects to verify part inspection compliance.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".