An Autoencoder with Convolutional Neural Network for Surface Defect Detection on Cast Components
Bibliographic record
Abstract
There is unrealized potential in using automation to alleviate the visual inspection associated with non-destructive testing in manufacturing facilities. The identification of defects during the production can help avoid substantial manufacturing errors by indicating that preventative maintenance should be introduced. The use of an autoencoder for this application reduces the need to generate datasets for various defect types, instead only one training dataset would be needed. To address this, this paper proposes a Convolution Neural Network (CNN) autoencoder approach to detect surface defects on cast components during the production. The proposed method categorizes the data into damaged and undamaged components by clustering based on the loss associated with the reconstructed image. The average F1-score and accuracy from retraining the model 10 times was 89.14% and 88.52% respectively. Although previous studies have obtained higher metrics, they have focused their efforts on supervised training techniques where as this research proposes an unsupervised training method with results comparable to the previous studies.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".