Enabling responsive real-time inspection of the automated fiber placement process
Bibliographic record
Abstract
Automated Fiber Placement (AFP) is used to manufacture large and complex parts in the aerospace industry. A time-consuming portion of this fabrication process remains the inspection and quality control, which are largely performed visually after each deposited layer. This conference proceeding showcases a disruptive, responsive, and reliable solution based on the Fives In-Process Inspection system enabled by the National Research Council of Canada (NRC) Optical Coherence Tomography (OCT) sensor to perform in-process defect monitoring of the fiber layup. Measurements are taken close to the material deposition location without inhibiting the optimal machine path or slowing down the layup process. Assessment of the quality of a deposition takes place concurrently while the layup head applies material. This responsive feedback loop enables adaptive control of the AFP fabrication process. Technical details on how the OCT based inspection system's data flow has been integrated within the manufacturing process of a Fives Viper AFP machine are provided to explain the system's real-time response and high-resolution measurements. Results obtained in an industrial setting using the sensor installed on a production AFP machine are presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".