Automated Fiber Placement inspection: enabling a paradigm shift in quality control towards high-fidelity surface profilometry
Bibliographic record
Abstract
Comparing manufactured parts to their engineering specifications is the basis for Quality Control. Traditionally, Geometric Dimensioning and Tolerancing (GD&T) standards define how engineering tolerances are used for generating fabrication specifications. Due to the complexities inherent to the composite layup process, Automated Fiber Placement (AFP) machines require a new paradigm of quality control. In addition to the traditional finished part dimension and quality reports, the AFP process requires a ply-by-ply inspection of the as-built laminate to ensure that each laydown remains within the manufacturing allowable specifications. To address this problem, Fives and the National Research Council Canada have proposed an In-Process Inspection system based on Optical Coherence Tomography (OCT) technology capable of performing high-resolution surface profilometry and automatic alignment of the as-manufactured measurements to the as-designed engineering model. With both an accurate surface profile and positioning of the measurement data in the CAD design reference, the differences can be analyzed to detect manufacturing anomalies and minimize process variability. Later on, this information has the fidelity required for an exact digital transformation of the process. This paper will review a few aspects of the Measurement System Analysis performed to validate the sensor’s reliability as well as review the high level methodology undertaken to establish the relationship between the IPI system’s sensor and the machine tool center point during fabrication. Finally, examples will be used to demonstrate the approach to obtain course, ply, and laminate level aggregations. Copyright 2021. Used by CAMX – The Composites and Advanced Materials Expo
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".