Automated Defect Detection for Epoxy-Carbon Prepreg Laminates in Data Fusion Approach
Bibliographic record
Abstract
Fibre-reinforced polymer composites have become widely used materials in the manufacturing of aerospace, boat building, and automotive, due to their high specific stiffness and strength, chemical resistance, etc. However, the curing process has a major influence on void content and fibre-matrix interface, affecting the quality of the composite part. In this work, non-destructive testing based on infrared thermography and shearography is used to detect the subsurface defects and impact damage in epoxy-carbon prepreg laminates. Different data fusion methods are used for the incrementing of the detection ability. For the requirement of industrial applications, an automated defect detection method named YOLOv7 is performed in the data fusion view. To improve the detection ability of YOLOv7, a data augmentation method named MixUp is used to construct the datasets obtained from the simulation. The experimental results show the excellent detection capacity of the proposed method. Furthermore, the experimental results also illustrate that the data fusion technique of the Dempster-Shafer method has the best effect compared with the other methods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".